An Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery

An Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery Sina Saadati, Maryam Hashemi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6338916/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial intelligence (AI) and computer vision are revolutionizing numerous fields, including robotic surgery, which stands to benefit immensely from advances in machine learning methodologies. While prior research has extensively focused on disorder detection, localization, and semantic segmentation, the crucial challenge of robotic arm maneuvering during autonomous surgeries remains underexplored. This study proposes a robust and interpretable approach to enable robots to autonomously execute endometriosis surgeries by skillfully navigating their arms, equipped with a camera and surgical tools such as graspers or lasers. A decision tree framework is developed to assess the robot's real-time status and guide its actions at every surgical stage. This framework integrates diverse ensemble neural network models for classification and semantic segmentation to support decision-making. Specifically, the proposed ensemble classification models utilize deep learning to assess image quality, identify obstructions caused by adhesions, detect anatomical targets (e.g., uterus or peritoneum), and determine the proximity of the ovary to the uterus. The proposed ensemble semantic segmentation models further enhance accuracy by detecting and localizing the uterus and ovary. By employing these ensemble frameworks within the proposed decision tree model, this work aims to advance robotic surgery capabilities, enabling fully autonomous, reliable, and efficient operations. Consequently, the proposed method aims to minimize economic costs, bleeding, post-operative pain, and infection risk, while optimizing surgical precision and performance. Artificial Intelligence and Machine Learning Fully-automated robotic surgery Artificial Intelligence in medicine Computer Vision in robotic surgery Detection and Segmentation for medical imaging medical image processing Figures Figure 1 Figure 2 Figure 3 1. Introduction AI, a pivotal subdiscipline of computer science, involves the development of systems capable of simulating human cognitive functions, including learning, reasoning, problem-solving, and decision-making [ 1 – 5 ]. Within the realm of surgeries and medical procedures, AI has emerged as a transformative innovation, driving advancements in medical analysis, diagnosis, drug discovery, personalized treatment recommendations, and autonomous surgical operations [ 6 – 11 ]. AI-powered systems offer significant advantages, such as superior accuracy even under high-pressure conditions, immunity to fatigue, and reduced energy consumption, which collectively translate into economic benefits for patients [ 8 ]. Their unparalleled reliability is attributed to the ability to process vast and complex datasets, leveraging rigorous engineering design principles that exceed the limitations inherent to human expertise. Consequently, AI has been increasingly integrated into a wide array of medical processes, from diagnostic procedures and drug development to surgical assistance, heralding a new era of precision and efficiency in healthcare [ 7 – 12 ]. The integration of robotics and computerization in surgical procedures brings numerous advantages. Robotic systems surpass human capabilities by performing surgeries with unparalleled precision, enhanced speed, and reduced energy requirements. Remarkably, these systems can execute parallel surgical operations on multiple areas of a patient’s body, significantly expediting procedures while maintaining exceptional accuracy—achievements that remain unattainable by human surgeons. As a result, the computerization of medical interventions has emerged as a rapidly advancing field of research and innovation, poised to transform the landscape of modern healthcare [ 6 – 12 ]. The automation of surgical procedures necessitates the application of AI methodologies, enabling computers to undertake medical and surgical tasks traditionally performed by human experts. To achieve this, AI systems must acquire the capability to learn from medical information, forming the basis for effectively executing surgeries [ 8 ]. Learning, a complex process observed in humans and animals, is extensively studied within the fields of neuroscience, biology, and AI. Research highlights that learning occurs within the human brain, a sophisticated network of interconnected neurons. Deep learning, a specialized branch of AI, focuses on the development of software systems that emulate the neurons and the neural architecture of the human brain. As a result, these systems are expected to exhibit learning capabilities analogous to those of the human brain. This approach, grounded in the simulation of neural structures within a software environment, is commonly referred to as Artificial Neural Networks (ANNs) [ 12 – 20 ]. Convolutional Neural Networks (CNNs), a specialized subset of ANNs, are inspired by the neural mechanisms of the human visual system. These networks employ convolutional and pooling layers that emulate the processes of human vision, enabling the interpretation and processing of visual data such as images and videos. CNNs are widely applied in tasks such as classification, semantic segmentation, and localization. For classification tasks, CNNs typically begin with a series of convolutional layers designed to extract features from input data by identifying patterns such as edges, textures, and shapes. These are followed by fully connected layers, which integrate the extracted features and perform the final classification. This hierarchical architecture allows CNNs to transform raw visual data into meaningful categories, which can be used for decision-making procedures [ 20 – 23 ]. U-Net models, an advanced subclass of CNNs, are distinguished by their U-shaped architecture, optimized for semantic segmentation and localization tasks. These tasks involve identifying the position of entities within an image, ranging from individuals depicted in images to medical conditions like cancerous cells or anatomical structures such as the uterus in robotic surgeries. U-Net models process input images through an encoder that extracts visual features, such as edges, textures, and colors. The extracted features are subsequently passed to a decoder, which performs pixel-level classification to generate a mask-like representation highlighting the location of the target entity within the original image [ 8 , 24 – 25 ]. Due to their robust performance, U-Net models are extensively employed in medical imaging applications, especially in scenarios requiring semantic segmentation and precise localization [ 24 – 25 ]. Transfer learning, a pivotal concept in deep learning, refers to the practice of applying pre-trained models—typically trained on large-scale datasets—to new tasks with smaller datasets. By leveraging the knowledge learned from a broader domain, transfer learning significantly reduces the time and computational resources required for training while improving accuracy [ 8 , 26 – 28 ]. In medical applications, transfer learning enables the adaptation of CNNs and U-Net models to specific tasks, such as detecting pathologies or segmenting anatomical structures, with limited training data[ 27 – 28 ]. Consequently, many studies, including the present work, utilize pretrained neural networks that have been previously trained on the ImageNet dataset, which comprises over 14 million images. This paper presents a reliable method for automating the movement and maneuvering of robotic arms and cameras in endometriosis robotic surgery. This method is relevant for AI-assisted surgeries and is vital for fully automated procedures, as described in [ 8 ]. To achieve this, the phases of a typical endometriosis surgical process are modeled as a decision-tree framework, in which nodes represent decision points or actions. Each decision-making node incorporates a reliable, accurate, and interpretable ensemble neural network classifier to assess the surgical state, identify the appropriate action, and proceed to the next step in the surgery. Here, "action" refers to the movement of robotic arms within the patient’s body. The proposed decision tree is designed to detect and address noise, such as obstructions on the camera lens caused by unexpected debris, including blood or water vapor. Additionally, it is structured according to the anatomical layout of the patient's body: the root node represents the robotic camera's entry point, while deeper nodes correspond to progressively deeper anatomical regions. Consequently, the proposed decision tree serves as an interpretable and adaptable model of surgical procedures. Interpretability is emphasized, as each processing unit's behavior is fully explainable. By segmenting surgical steps into small atomic phases and integrating a robust ensemble neural network framework for each phase, the proposed method optimizes both interpretability and reliability. The proposed ensemble frameworks enhance both the accuracy and reliability of the system. Assigning the same task to different neural networks with varying architectures enables the identification of similar patterns while capturing distinct noise. Integrating these networks into an ensemble framework allows for error correction, as the minimal overlap in individual neural network errors ensures overall higher robustness [ 7 ]. Our findings also demonstrate that the ensemble neural network significantly improves system accuracy. Consequently, the proposed decision tree, which guides the robotic surgeon through endometriosis surgery, combined with the ensemble neural network frameworks at each decision-making node, establishes a reliable and robust system. Endometriosis, the condition forming the basis for the surgeries discussed in this paper, is a prevalent health issue among women. It occurs when tissue similar to the lining of the uterus grows outside the uterine cavity, often leading to chronic pain and other complications. This condition commonly affects the pelvic region, including the ovaries, uterus, and peritoneum. Endometriosis can cause severe pain, particularly during menstruation, as well as infertility, fatigue, and other complications that significantly impact the quality of life. Treatment approaches vary depending on the severity of the disease, ranging from medication to alleviate symptoms, to surgical interventions aimed at removing endometrial tissue and restoring normal organ function[ 8 , 29 ]. Robotic surgery, as discussed in this paper, represents a cutting-edge approach that offers precision and adaptability in treating complex cases of endometriosis[ 8 , 29 – 31 ]. Contribution of this paper are as follows: Decision-Making Tree for Surgical Guidance : Design and development of a reliable decision-tree model to guide the robotic surgeon through the endometriosis surgery and automating its arm maneuvering and movements. Four Ensemble Neural Network Frameworks for State Detection : Design and implementation of four ensemble neural network frameworks for classification to analyze the surgical state, make decisions, and conduct the robotic surgery. Two Ensemble Semantic Segmentation and Localization Frameworks : Design and development of accurate and reliable ensemble frameworks for semantic segmentation and localization, enabling detection and positioning of the uterus and ovaries within real-time images during robotic endometriosis surgery. This paper is structured as follows: Section 2 reviews the existing literature, situating the proposed research within the context of current studies. Section 3 provides a detailed explanation of the proposed methodology. Section 4 evaluates the proposed method through rigorous assessments. Finally, Section 5 discusses the findings and proposes directions for future research. 2. Literature Review Recent research has extensively investigated the automation of medical procedures, with significant emphasis on the analysis and diagnosis of diseases using advanced computational techniques. For instance, deep learning methods have been employed to process mammographic images for breast cancer detection and diagnosis, as explored in studies [ 32 – 37 ]. Similarly, motion disorders such as Parkinson's disease have been a focal point of automation research, as detailed in [ 38 ]. Another study addresses the analysis of human motion to determine whether it is normal or pathological, emphasizing the importance of interpretability in intelligent medical applications [ 7 ]. This focus on interpretability underscores the necessity for intuitive algorithms that enhance system reliability, while also facilitating maintenance and future reconfigurations. Progress in automation spans diverse medical fields, including oncology, motion disorders, psychological disorders, and infectious diseases. However, despite the advancements in these domains, research on automating surgical procedures remains comparatively limited. The automation and computerization of surgical processes, as this paper examines, offer the potential to significantly improve both medical and clinical outcomes, paving the way for more precise and efficient healthcare solutions. The integration of surgical procedures with AI and deep learning methodologies holds immense potential to enhance surgical accuracy, efficiency, and reliability. Nezhat et al. introduced the concept of utilizing AI applications in surgical practices, highlighting their ability to significantly advance medical and surgical processes. This integration also offers the prospect of standardizing surgical procedures by reducing variability in techniques employed by different surgeons for the same operation, thereby promoting consistency in surgical practices. Despite the promising outlook, Nezhat et al. did not propose a concrete methodology for achieving surgical automation through AI approaches [ 39 ]. Recent studies have explored the automation of pathology detection during surgeries, leading to notable advancements. For instance, the automation of endometriosis detection during laparoscopic surgeries has been demonstrated in [ 40 ]. Another relevant approach is discussed in [ 41 ]. However, despite these developments, the full computerization of surgical procedures remains unattained, as surgeons continue to perform critical tasks. Consequently, there is substantial scope for improvement in areas such as accuracy, cost-efficiency, procedural speed, bleeding control, and infection risk mitigation during surgeries, which can be promoted by performing a fully-automatic surgery. Our previous work, detailed in [ 8 ], introduced a reliable method for the full automation of robotic surgeries, with a focus on endometriosis surgery. This study implemented the automation of detection, semantic segmentation, and localization of endometriotic lesions through a dependable and interpretable computer vision framework. Furthermore, an algorithm for fully automating the surgical process was proposed. However, the maneuvering of robotic cameras and arms was designed based on geometric data derived from the anatomical structures of the patient's body. To further advance the intelligent navigation of robotic arms, there is a need for a methodology that guides the robot through the sequence of surgical steps. This phase can be significantly improved by developing more robust frameworks that leverage advanced ensemble neural network techniques. From the perspective of robotic maneuvering and arm movement, an interpretable method for the automatic control of robotic arms during surgery is proposed in [ 42 ]. This work introduces the Sina Tree, a framework designed based on the anatomical structure of human organs. However, further research is needed to explore the effective integration of computer vision techniques into the Sina Tree framework. Building upon this foundation, the current study presents a reliable decision-tree model enhanced by advanced ensemble neural network frameworks. The proposed model is designed to analyze the surgical context at each step, make informed decisions, and systematically guide the procedure during surgery. 3. Proposed Methodology This section outlines the proposed method for automating the maneuvering of robotic arms within the patient’s body in an endometriosis surgery. A decision-tree model is introduced, designed specifically to guide robotic systems step-by-step through typical endometriosis surgery procedures. The proposed tree comprises decision-making nodes, where data from the camera and other metadata inputs are analyzed to determine appropriate actions, such as proceeding with the surgery or halting it in response to an identified error. Additionally, the tree includes action nodes that dictate specific tasks, such as robotic arm and camera movements. Furthermore, advanced ensemble neural network frameworks have been developed and integrated into the decision-making nodes to enhance the system’s analytical and operational capabilities. In the first step of the proposed decision-tree, the system evaluates whether the camera captures a high-quality image. In certain cases, visual obstructions, such as blood particles or water vapor, can severely impair the robot's vision by introducing noise into the images. Such noisy images can lead to significant deviations from the robot’s intended operational strategy. Therefore, as a preliminary measure, the robot must assess the quality of the visual data received from the camera. If the image is excessively noisy, the system should notify clinical staff to address the issue. Alternatively, the robot can be equipped with mechanisms to autonomously clean the camera’s lens, thereby reducing reliance on clinical personnel and facilitating the surgical process. In some patients, adhesions may be present, posing obstacles during surgery. To address this, surgeons typically use a CO2 laser to vaporize the adhesions, thereby clearing the surgical field and facilitating the operation. Accordingly, in the second step of the proposed decision-tree, following confirmation that the images are free of noise, the robot must determine whether adhesions are present within the patient’s body. If adhesions are detected, the robot should employ the CO2 laser instrument to eliminate them and streamline the surgical process. Conversely, if no adhesions are found, the robot should proceed to the third step, wherein it determines whether to examine gynecological positions (the uterus and ovaries) or the peritoneum. In the third step of the proposed method, the robot confirms that the visual data is noise-free and that no adhesions are present within the body. Following this, the robot proceeds to survey the ovaries, uterus, or peritoneum to identify potential ovarian, uterine, or peritoneal endometriosis lesions. To achieve this, the robotic camera must rotate toward the upper region (the peritoneum) or the uterus. After performing this rotation, the system verifies whether the peritoneum or uterus is visible through the camera in order to certify the rotation. This process mimics the cognitive approach of laparoscopic surgeons, who rotate the laparoscope and observe the monitor to determine whether the peritoneum or uterus is within the field of view. However, there is a minimal risk of rotation failure during this step. If such an issue arises, the system should alert nurses and clinical experts to intervene and resolve the problem. While the probability of rotation failure is minimal, it must still be considered, given the critical importance of achieving fully autonomous surgery. Addressing even low-probability risks ensures maximum reliability of the system. If the peritoneum is selected for investigation, the robot must systematically survey its various regions to identify potential endometriosis lesions. Upon detecting such lesions, the robot can utilize a CO2 laser to effectively vaporize them. Using a CO2 laser is considered effective for eliminating pathological tissue and offers distinct advantages over traditional surgical approaches [ 8 ]. Alternatively, if the uterus or ovaries are chosen for examination, the robotic arms must advance toward the uterus. At this stage, the robot is required to detect, semantically segment, and localize the uterus. To achieve this, a uterus semantic segmentation framework is employed. This framework not only enables the robot to maneuver its arms with precision but also assists in calculating the proximity of the robotic arms and camera to the uterus. Specifically, a larger segmented surface area of the uterus indicates a closer proximity of the camera to the uterus. To ensure safe movement of the camera toward the uterus, the distance between the uterus and the camera must be accurately calculated. This calculation is crucial for guiding the camera forward while preventing any potential collision with the uterus or surrounding tissues. After reaching the uterus, it must be held and stabilized to allow for meticulous and careful examination. We propose the use of suction instruments instead of traditional grasping tools, as suction instruments not only simplify surgical procedures but also minimize potential damage to the uterine tissue. To achieve this, the uterus semantic segmentation framework is essential. This framework facilitates the process of stabilizing the uterus for the investigation of uterine endometriosis and assists in positioning and holding the uterus to access the ovaries for the identification of ovarian endometriosis. For cases of ovarian endometriosis, a similar strategy should be employed following the stabilization of the ovaries using either grasping instruments or suction instruments. Subsequently, the ovary must be carefully detected and localized. To achieve this, an ovary semantic segmentation framework is required, enabling the simultaneous detection and localization of both the ovary and uterus for the purpose of identifying ovarian endometriosis. Finally, upon accurately detecting, localizing, and segmenting endometriosis lesions—whether on the peritoneum, uterus, or ovaries—the robot can utilize its CO2 laser to vaporize the pathological tissue. The proposed decision-tree model is illustrated in Fig. 1 . For the endometriosis lesions detection and localization, the ensemble framework introduced in [ 8 ] can be used. In the proposed decision-tree framework, four decision-making nodes are designed to address noise detection, adhesion detection, peritoneum or uterus classification, and ovary detection, respectively. For each decision-making node, an advanced ensemble neural network framework has been developed to perform binary classification. Additionally, two semantic segmentation and localization frameworks are implemented to identify the uterus and ovaries during surgery, which are essential for determining organ positions and enabling appropriate surgical actions. The ensemble frameworks enhance the reliability of the proposed system by integrating multiple AI models, thereby yielding more precise decisions. This approach can be compared to a scenario where a panel of experts deliberates on a problem, resulting in more reliable and trustworthy outcomes. Furthermore, as the neural networks within the ensemble framework operate in parallel, there is no significant impediment to the overall execution speed. The structures of the proposed ensemble neural network classifiers are designed with an initial layer that extracts visual features from the input image. This layer employs various architectures, such as ResNet and VGG, among others. The extracted features are then passed to a fully connected layer responsible for determining the class to which the input image belongs. Based on this classification, the network generates a vote. Simultaneously, additional neural networks employing different architectures process the input image, each producing its own votes. Finally, a collective decision is made by aggregating the votes from the individual networks. This final decision guides the robot through the decision-making nodes in the proposed decision-tree model. The structure of the ensemble neural network classifiers, incorporating diverse architectures as feature extractors, is illustrated in Fig. 2. The architecture of the proposed ensemble neural network classifier, which is designed to determine whether the input image is noisy or of high quality, is illustrated in Fig. 2.a. In each neural network, the image or frame from the real-time video is initially processed by a feature extractor layer. This layer can be implemented using various CNN architectures, such as ResNet, VGG, and others. The extracted visual features are subsequently passed to a fully connected layer (FCL), which determines the category to which the input image belongs. This methodology is uniformly applied across all proposed ensemble classification frameworks. However, the frameworks differ in the number of feature extractor layers and the specific architectures utilized. To design the ensemble neural network frameworks, a systematic approach was employed. First, various architectures were examined for each classification task. Subsequently, the outputs of all neural networks for the same test set of images were recorded. Finally, a search algorithm was utilized to evaluate all possible designs for the ensemble framework. The ensemble with the highest accuracy was ultimately selected for inclusion in the proposed system. In cases where multiple ensemble frameworks achieved the same accuracy, the framework with a greater number of networks was chosen to enhance the system's reliability. Since the output of the ensemble framework is generated by a larger number of advanced networks, the results are anticipated to be more robust and dependable. Our findings demonstrate that the ensemble neural network framework, incorporating feature extractor architectures such as ResNet50, ResNet50V2, VGG16, VGG19, ResNet101, ResNet101V2, InceptionResNetV2, DenseNet169, DenseNet201, and NASNetLarge, achieved the highest reliability for the first decision-making node of the proposed tree. This node is responsible for classifying noisy images from high-quality ones. In this design, the threshold value, set to 2, determines that if the number of votes within an ensemble framework meets or exceeds this value, the classifier produces a positive output. For the first ensemble framework, a positive output corresponds to the noisy image class. The architecture of this framework is depicted in Fig. 2.a. Similarly, the proposed ensemble framework for the second decision-making node in the decision tree comprises 14 independent neural networks employing various feature extractor architectures, including InceptionV3, MobileNet, ResNet50, ResNet50V2, VGG16, VGG19, ResNet101, ResNet101V2, InceptionResNetV2, DenseNet121, DenseNet169, DenseNet201, NASNetLarge, and ConvNeXtXLarge. The threshold value for the second ensemble framework is set to 10. This framework is designed to classify images containing adhesions from normal images, with its architecture illustrated in Fig. 2.b. For the third decision-making node in the proposed tree, an ensemble framework was developed to classify whether the input image contains the uterus or peritoneum. Our findings indicate that the ensemble framework incorporating feature extractor layers with architectures such as InceptionV3, ResNet50, ResNet50V2, VGG16, VGG19, ResNet101, ResNet101V2, InceptionResNetV2, DenseNet169, DenseNet201, MobileNet, and NASNetLarge demonstrates sufficient reliability. The threshold value for this ensemble framework is set to 1. The architecture of this framework is illustrated in Fig. 2.c. Similarly, for the fourth decision-making node in the proposed tree, the ensemble classifier utilizes InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, InceptionResNetV2, VGG16, VGG19, MobileNet, DenseNet121, DenseNet169, ConvNeXtXLarge, and NASNetLarge as the feature extractor architecture. The threshold value for this framework is set to 10. The architecture of the proposed ensemble framework for the fourth decision-making node is depicted in Fig. 2.d. In the proposed decision tree framework, semantic segmentation and localization tasks are handled using two ensemble frameworks—one dedicated to uterus detection and the other to ovary detection. Each ensemble framework integrates three U-Net models, employing ResNet50, ResNet101, and VGG19 architectures as backbone networks. Within the decision tree, the segmentation and localization frameworks for the uterus and ovary are executed following classification at the third and fourth decision-making nodes. While ovary detection can be performed without a classification framework, this approach has been designed to minimize noise-related risks, thereby improving the overall reliability of the system. The architectures of the proposed semantic segmentation frameworks for uterus and ovary detection are depicted in Figs. 3.a and 3.b, respectively. In ensemble frameworks for classification, each neural network generates a binary decision that identifies the category to which the input image belongs. Similarly, in semantic segmentation frameworks, each neural network produces binary decisions for individual pixels within the input image. To achieve accurate semantic segmentation and localization of the uterus and ovary during robotic and laparoscopic surgery, the votes from all neural networks are aggregated for each pixel. As detailed in Section 4 , our findings reveal that the proposed neural network frameworks for both classification and semantic segmentation tasks exhibit robustness and reliability. Moreover, the decision tree, developed based on medical methodologies for endometriosis surgery and integrated with advanced deep learning frameworks, enhances system reliability. The interpretability of the decision tree further supports its adaptability and ease of maintenance for future applications. By presenting a dependable approach for maneuvering robotic arms within a patient's body during surgery, the proposed method significantly improves the efficiency and precision of surgeries. When combined with advanced detection techniques, as referenced in [ 8 ], this approach enables fully automated surgical procedures powered by reliable AI systems. This innovation not only enhances surgical accuracy and reliability but also reduces the risks of post-operative infections, decreases economic costs, and mitigates intraoperative bleeding. 4. Evaluation In this section, the proposed method is validated through a comprehensive evaluation of all modules within the proposed ensemble framework. The ensemble architecture not only enhances the system’s accuracy but also improves its reliability. This is because different neural networks with varying architectures, when trained on the same dataset, tend to identify consistent patterns while learning different noises. Consequently, the ensemble framework mitigates unexpected noise and errors by employing a consensus-based approach. The initial step of the system occurs at the beginning of the surgical procedure, where it verifies whether the camera is functioning correctly or obstructed by elements such as debris or blood. For this phase, multiple deep neural networks with diverse architectures are developed. These networks are integrated into an ensemble framework to construct a system capable of determining whether the input image depicts the internal body, intense tissue adhesions that impair visibility, or camera malfunctions producing severe noise (e.g., fully white, fully dark, or completely blurred screens). If the camera correctly captures the internal body, the robotic system advances the camera to explore deeper regions. In cases where intense adhesions are detected, the robot employs a CO2 laser to vaporize the tissue and proceed with the surgery. For severe noise, the system alerts medical staff to address the issue manually. In this study, scenarios involving severe noise in camera imagery are classified as abnormal states, while tissue adhesions or normal internal body conditions are categorized as normal states. A binary classification approach is developed using deep neural networks to distinguish between these conditions. Finally, a full binary tree is employed to identify the optimal ensemble architecture, utilizing networks such as ResNet50. An image is classified as abnormal only if two or more networks within the ensemble detect it as abnormal. The evaluation results of the ensemble neural network framework for the initial phase of the surgical procedure are presented in Table 1 . As demonstrated, the ensemble network achieves the highest accuracy compared to individual neural networks. These findings indicate that the ensemble framework effectively minimizes errors while maximizing both accuracy and reliability. Neural networks with lower accuracy were excluded from the ensemble framework to ensure optimal performance. Using the individual neural networks for the proposed ensemble framework is reported as ‘Ensemble Module’ in Table 1 . Table 1, The evaluation results for the neural network framework for the first step of the surgery. Architecture Train Accuracy Validation Accuracy Test Accuracy Precision Recall F1-Score Specificity Ensemble Module ResNet50 99.19 90.07 98.00 99.32 96.67 97.97 99.34 ü ResNet50V2 100.0 95.00 98.34 100.0 69.67 98.31 100.0 ü VGG19 100.0 94.50 98.34 100.0 96.67 98.31 100.0 ü VGG16 100.0 95.00 96.34 100.0 92.67 96.19 100.0 ü ResNet101 98.62 96.00 90.67 100.0 81.34 89.71 100.0 ü ResNet101V2 100.0 94.00 95.34 97.89 92.67 95.21 98.00 ü InceptionV3 100.0 92.50 94.67 94.67 94.67 94.67 97.64 × InceptionResNetV2 99.86 98.50 94.00 100.0 88.00 93.62 100.0 ü MobileNet 99.94 94.00 94.67 92.41 97.34 94.81 92.00 × DenseNet121 100.0 97.00 96.67 96.05 97.34 96.69 96.00 × DenseNet169 100.0 84.50 95.34 100.0 90.67 95.10 100.0 ü DenseNet201 100.0 90.00 98.67 100.0 97.34 98.65 100.0 ü NASNetLarge 99.63 93.00 87.67 100.0 75.34 85.93 100.0 ü EfficientNetB1 52.29 50.00 50.00 50.00 100.0 66.67 0.0 × EfficientNetB3 52.23 50.00 50.00 50.00 100.0 66.67 0.0 × EfficientNetB5 50.87 50.00 50.00 50.00 100.0 66.67 0.0 × EfficientNetB7 49.70 50.00 50.00 50.00 100.0 66.67 0.0 × ConvNeXtXLarge 99.50 92.50 87.34 93.75 80.00 86.33 94.67 × Proposed Ensemble Framework --- --- 99.67 100.0 99.34 99.67 100.0 Similarly, for the ensemble frameworks proposed for the second, third, and fourth steps of the decision tree, evaluation processes were designed, demonstrating that the frameworks are both accurate and reliable. In each framework, the final decision is generated through the parallel execution of multiple diverse neural networks, thereby maximizing system reliability. The results for the proposed ensemble framework, which classifies input images as containing adhesions or being adhesion-free, are presented in Table 2 . Table 2, The evaluation results for the neural network framework for the second step of the surgery. Architecture Train Accuracy Validation Accuracy Test Accuracy Precision Recall F1-Score Specificity Ensemble Module ResNet50 99.65 97.00 100.0 100.0 100.0 100.0 100.0 ü ResNet50V2 100.0 99.00 90.13 83.85 100.0 91.22 79.73 ü VGG19 100.0 84.50 90.45 84.29 100.0 91.48 80.39 ü VGG16 93.85 97.50 92.36 87.03 100.0 93.06 84.31 ü ResNet101 99.73 96.00 100.0 100.0 100.0 100.0 100.0 ü ResNet101V2 100.0 98.00 90.13 83.85 100.0 91.22 79.74 ü InceptionV3 100.0 99.50 100.0 100.0 100.0 100.0 100.0 ü InceptionResNetV2 96.14 74.50 84.39 76.67 100.0 86.79 67.97 ü MobileNet 98.46 97.50 70.38 63.39 100.0 77.59 39.22 ü DenseNet121 100.0 100.0 94.90 90.96 100.0 95.27 89.54 ü DenseNet169 100.0 96.00 80.25 72.20 100.0 83.85 59.48 ü DenseNet201 100.0 82.50 67.52 61.22 100.0 75.94 33.33 ü NASNetLarge 96.16 93.50 91.72 86.10 100.0 92.53 83.01 ü EfficientNetB1 53.50 50.00 51.27 51.27 100.0 67.79 0.0 × EfficientNetB3 50.79 50.00 51.27 51.27 100.0 67.79 0.0 × EfficientNetB5 48.15 50.00 51.27 51.27 100.0 67.79 0.0 × EfficientNetB7 51.58 50.00 51.27 51.27 100.0 67.79 0.0 × ConvNeXtXLarge 100.0 87.00 89.49 82.99 100.0 90.70 78.43 ü Proposed Ensemble Framework --- --- 100.0 100.0 100.0 100.0 100.0 Table 3, The evaluation results for the neural network framework for the third step of the surgery Architecture Train Accuracy Validation Accuracy Test Accuracy Precision Recall F1-Score Specificity Ensemble Module ResNet50 96.05 98.41 100.0 100.0 100.0 100.0 100.0 ü ResNet50V2 93.05 93.65 68.50 100.0 37.00 54.01 100.0 ü VGG19 96.12 93.65 60.00 100.0 20.00 33.34 100.0 ü VGG16 89.63 93.65 64.00 100.0 28.00 43.75 100.0 ü ResNet101 100.0 96.83 100.0 100.0 100.0 100.0 100.0 ü ResNet101V2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 ü InceptionV3 100.0 94.44 96.50 100.0 93.00 96.37 100.0 ü InceptionResNetV2 87.63 93.65 59.00 100.0 18.00 30.51 100.0 ü MobileNet 93.51 97.62 98.50 100.0 97.00 98.48 100.0 ü DenseNet121 90.02 93.65 57.00 100.0 0.14 24.56 100.0 × DenseNet169 100.0 100.0 100.0 100.0 100.0 100.0 100.0 ü DenseNet201 95.93 94.44 84.00 100.0 68.00 80.95 100.0 ü NASNetLarge 100.0 93.65 78.50 100.0 57.00 72.61 100.0 ü EfficientNetB1 50.23 50.00 50.00 0.0 0.0 0.0 100.0 × EfficientNetB3 70.87 50.00 70.50 75.95 60.00 67.04 81.00 × EfficientNetB5 53.89 50.00 50.00 0.0 0.0 0.0 100.0 × EfficientNetB7 52.58 50.00 50.00 0.0 0.0 0.0 100.0 × ConvNeXtXLarge 100.0 92.86 60.00 100.0 20.00 33.34 100.0 × Proposed Ensemble Framework --- --- 100.0 100.0 100.0 100.0 100.0 For the third step of the proposed decision tree model, an ensemble framework was developed and evaluated, demonstrating its ability to accurately and reliably classify input images as either peritoneum or uterus images. The evaluation results for this framework are presented in Table 3. Similarly, the evaluation details for the fourth ensemble framework, which classifies images as depicting a uterus with or without an ovary, are provided in Table 4. Table 4, The evaluation results for the neural network framework for the fourth step of the surgery. Architecture Train Accuracy Validation Accuracy Test Accuracy Precision Recall F1-Score Specificity Ensemble Module ResNet50 95.04 100.0 99.08 100.0 98.18 99.08 100.0 ü ResNet50V2 99.12 100.0 78.90 72.86 92.73 81.60 64.81 ü VGG19 75.43 100.0 79.82 72.00 98.18 83.08 61.11 ü VGG16 76.09 100.0 75.23 67.50 98.18 80.00 51.85 ü ResNet101 90.59 100.0 88.99 83.08 98.18 90.00 79.63 ü ResNet101V2 99.73 100.0 97.25 96.43 98.18 97.30 96.30 ü InceptionV3 97.89 100.0 96.33 96.36 96.36 96.36 96.30 ü InceptionResNetV2 82.78 98.65 82.57 75.00 98.18 85.04 66.67 ü MobileNet 99.24 100.0 79.82 72.00 98.18 83.08 61.11 ü DenseNet121 99.38 100.0 95.41 98.08 92.73 95.33 98.15 ü DenseNet169 99.79 97.30 83.47 76.07 98.18 85.71 68.52 ü DenseNet201 100.0 100.0 88.99 89.09 89.09 89.09 88.88 × NASNetLarge 99.76 100.0 87.16 80.60 98.18 88.52 75.93 ü EfficientNetB1 46.89 51.35 50.46 50.45 100.0 67.07 0.0 × EfficientNetB3 48.48 51.35 50.46 50.46 100.0 67.07 0.0 × EfficientNetB5 51.83 48.65 50.46 50.46 100.0 67.07 0.0 × EfficientNetB7 52.55 54.05 50.46 50.46 100.0 67.07 0.0 × ConvNeXtXLarge 99.40 100.0 84.40 88.00 80.00 83.81 88.89 ü Proposed Ensemble Framework --- --- 99.08 100.0 98.18 99.08 100.0 The evaluation results indicate that the proposed decision tree model, equipped with the ensemble frameworks, achieves high accuracy and reliability. Furthermore, as the neural networks within the frameworks operate in parallel, the system maintains computational efficiency. This rapid performance is crucial for conducting surgical procedures at the highest possible speed, minimizing risks of infection, bleeding, and post-operative pain. The proposed decision-tree model incorporates not only ensemble classifiers but also semantic segmentation and localization frameworks for detecting the uterus and ovaries during surgery. To achieve this, U-Net models were developed to identify the uterus and ovaries effectively. Subsequently, two ensemble frameworks were designed for detecting the uterus and ovaries, respectively. Each framework demonstrates accuracy, reliability, and interpretability, as they are designed to detect simple patterns. Table 5 details the evaluation metrics for the proposed semantic segmentation frameworks. This approach contrasts with some studies where a single neural network is tasked with identifying multiple concepts. Consequently, the overall system, proposed to automate the robotic maneuvering of arms, cameras, and instruments within the patient’s body during endometriosis surgery, is highly reliable. Intersection over Union (IoU) is a metric commonly used to assess the accuracy of semantic segmentation tasks by measuring the overlap between predicted segmentation and ground truth. It is calculated as the ratio of the intersection area to the union area of the predicted and ground truth segments, capturing both false positives and false negatives. IoU provides a quantitative evaluation of segmentation performance, with higher values indicating better alignment between predictions and actual segments. In this study, IoU is used to evaluate the effectiveness of the proposed semantic segmentation frameworks for detecting the uterus and ovaries, with the results presented in Table 5 to demonstrate the system's precision in localization and segmentation during surgery. Table 5, The evaluation results for the proposed uterus and ovary semantic segmentation and localization frameworks. U-Net backbone architecture Uterus Localization Ovary Localization Train IoU Validation IoU Test IoU Train IoU Validation IoU Test IoU ResNet50 97.63 95.20 98.10 92.34 94.21 93.77 ResNet101 97.85 98.67 96.03 95.10 91.56 93.84 VGG19 99.12 95.46 97.42 91.88 94.55 92.89 Proposed Ensemble Framework 97.52 98.16 97.98 92.75 92.98 93.64 By integrating this methodology with other techniques for fully automated surgeries, surgical and medical procedures can be performed with greater accuracy, faster execution, reduced economic costs for patients and clinical centers, lower risks of postoperative infections and pain, minimized bleeding, and faster recovery. Furthermore, the proposed method is adaptable to the automation of other surgical operations, such as cancer-related procedures. Since the proposed robotic surgeons are based on knowledge-based programs, and given that such knowledge can be easily enhanced, transferred, and replicated within computer systems, this methodology has the potential to significantly increase access to medical and clinical facilities—even in regions with acute shortages of medical devices, specialists, and experienced surgeons. All that is required for surgery is a robot connected to the Internet, which can be easily accessible in many parts of the world. 5. Discussion and Future Works In this paper, an intelligent and reliable method is proposed to automate robotic maneuvering during robotic endometriosis surgeries. The proposed approach enables fully automated surgical procedures, offering several advantages, including reduced bleeding, accelerated operation times, minimized risk of infection, diminished postoperative pain due to shorter surgery durations, decreased economic costs, and improved surgical accuracy. The methodology involves dividing the surgical process into atomic phases, each analyzed using an ensemble neural network framework. This framework is designed to detect the status of each step and determine the appropriate subsequent actions. To enhance the reliability of the approach, rare challenges, such as camera noise and adhesions within the patient’s body, are considered. Additionally, a binary decision tree is introduced to enable fully automated maneuvering of robotic arms during surgeries. This decision tree incorporates ensemble neural networks at each decision-making node, allowing the robotic arms to operate reliably based on the detected status of the surgery at every decision point. In this paper, endometriosis, a common condition affecting women, is used as the foundation for the surgical framework. Consequently, the proposed decision-making tree is specifically designed for ovarian or uterine endometriosis surgeries. However, the methodology can be extended to develop reliable decision-making trees for other types of surgeries, such as those for cancer treatment. This extension will be explored in future research. While this paper proposes an intelligent and reliable framework to guide robotic surgeons during endometriosis surgeries, further advancements can be achieved by exploring additional computing techniques, such as cloud computing. A cloud-based strategy offers advantages such as more efficient computation and simple centralized updates. However, implementing this strategy necessitates the design and development of secure data stream channels. To maximize the effectiveness of the proposed method, future work will also focus on enhancing the hardware responsible for executing the computational processes. In addition, while valuable datasets for gynecological research currently exist, the proposed deep learning systems can be strengthened by incorporating larger and more diverse datasets. These could include additional laparoscopic and robotic surgery videos and images. Providing such data for research and development purposes is another critical objective planned for future investigations. References Winston PH (1992) Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc. Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. pearson Ertel W (2024) Introduction to artificial intelligence. Springer Nature Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning, vol 4. springer, New York, p 738. 4 Burkov A (2020) Machine learning engineering, vol 1. True Positive Incorporated, Montreal, QC, Canada Fieschi M (2013) Artificial intelligence in medicine: Expert systems. Springer Saadati S, Sepahvand A, Razzazi M (2025) Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder. https://doi.org/10.1016/j.heliyon.2025.e42119 . 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Springer International Publishing, Berlin/Heidelberg, Germany, p 6330 Siddique N, Paheding S, Elkin CP, Devabhaktuni V (2021) U-net and its variants for medical image segmentation: A review of theory and applications. IEEE access 9:82031–82057 Du G, Cao X, Liang J, Chen X, Zhan Y (2020) Medical image segmentation based on U-net: A review. J Imaging Sci Technol, 64(2) Gupta J, Pathak S, Kumar G (2022), May Deep learning (CNN) and transfer learning: a review. In Journal of Physics: Conference Series (Vol. 2273, No. 1, p. 012029). IOP Publishing Hussain M, Bird JJ, Faria DR (2019) A study on CNN transfer learning for image classification. Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5–7, 2018, Nottingham, UK. Springer International Publishing, pp 191–202 Salehi, A. W., Khan, S., Gupta, G., Alabduallah, B. I., Almjally, A., Alsolai, H.,… Mellit, A. (2023). A study of CNN and transfer learning in medical imaging: Advantages,challenges, future scope. Sustainability, 15(7), 5930 Nezhat, C., Li, A., Falik, R., Copeland, D., Razavi, G., Shakib, A., … Nezhat, F.(2018). Bowel endometriosis: diagnosis and management. American journal of Obstetrics and Gynecology, 218(6), 549–562 Siesto G, Ieda N, Rosati R, Vitobello D (2014) Robotic surgery for deep endometriosis: a paradigm shift. Int J Med Rob Comput Assist Surg 10(2):140–146 Nezhat CR, Stevens A, Balassiano E, Soliemannjad R (2015) Robotic-assisted laparoscopy vs conventional laparoscopy for the treatment of advanced stage endometriosis. J Minim Invasive Gynecol 22(1):40–44 Xia, L., An, J., Ma, C., Hou, H., Hou, Y., Cui, L., … Gao, Z. (2023). Neural network model based on global and local features for multi-view mammogram classification.Neurocomputing, 536, 21–29 Nissar I, Alam S, Masood S, Kashif M (2024) MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms, vol 248. Computer Methods and Programs in Biomedicine, p 108121 Sahu A, Das PK, Meher S (2024) An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images. Biomed Signal Process Control 87:105377 Joseph, A. J., Dwivedi, P., Joseph, J., Francis, S., Pournami, P. N., Jayaraj, P.B., … Sankaran, P. (2024). Prior-guided generative adversarial network for mammogram synthesis. Biomedical Signal Processing and Control, 87, 105456 Tiryaki VM (2023) Mass segmentation and classification from film mammograms using cascaded deep transfer learning. Biomed Signal Process Control 84:104819 Yurdusev AA, Adem K, Hekim M (2023) Detection and classification of microcalcifications in mammograms images using difference filter and Yolov4 deep learning model. Biomed Signal Process Control 80:104360 Singh K, Khare M, Khare A, Kohli N (2025) Review on computational methods for the detection and classification of Parkinson's Disease. Comput Biol Med 187:109767 Nezhat FR, Kavic M, Nezhat CH, Nezhat C (2023) Forward we go! JSLS: J Soc Laparosc Robotic Surg 27(1):e2022–00073 Zaidi SA, Chouvatut V, Phongnarisorn C, Praserttitipong D (2025) Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation. Intelligence-Based Med, 100230 Macis, C., Santoro, M., Zybin, V., Di Costanzo, S., Coada, C. A., Dondi, G., … Strigari,L. (2025). A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Related Ovarian Cancer. Applied Sciences, 15(6), 3070. Saadati S (2023) Nahid: AI-based Algorithm for operating fully-automatic surgery. arXiv preprint. https://doi.org/10.48550/arXiv.2401.08584 . arXiv:2401.08584 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6338916","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435936793,"identity":"d699965a-3de7-4688-9ffb-d7ee188bda0a","order_by":0,"name":"Sina Saadati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACZhjjABB9gHESiNPCzHBwBgODBGEtcADUwswD04IPyLfzHvzA8MdOnu/4+YOHbWoO1zGwH37A8HAPbi0Gh/mSJRjbkg1nnklmOJxz7LAEA0+aAUPCMzxamHkMJBgbmBk3HABpYQNqYcgB+uUAHoc18xj/YPhTb7/h/GOGwxb/gFr43+DXwnCYx0yCge1w4oYbQFsY24BaJAjYYgDUYpHYdjx55o3HBgd7+9Il2ySeGRzA67D+M8Y3Pvyptu07n/j4w49v1vz8/MkPH/7A5zAQSEDmsDGAU8IoGAWjYBSMAkoAAAtYUPCje5C1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2132-9879","institution":"Department of Computer Engineering, Amirkabir University of Technology(Tehran Polytechnic), Tehran, Iran.","correspondingAuthor":true,"prefix":"","firstName":"Sina","middleName":"","lastName":"Saadati","suffix":""},{"id":435936794,"identity":"538048f7-0a3d-41ea-bc5d-a390c32284cb","order_by":1,"name":"Maryam Hashemi","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, Assistant Professor of School of Medicine, Isfahan University of Medical Science, Isfahan, Iran.","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Hashemi","suffix":""}],"badges":[],"createdAt":"2025-03-30 14:15:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6338916/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6338916/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79765750,"identity":"12766f4f-1999-4531-ac50-fab571e986ed","added_by":"auto","created_at":"2025-04-02 12:11:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":761653,"visible":true,"origin":"","legend":"\u003cp\u003eThe structure of proposed decision-tree for leading the robot during the surgery.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6338916/v1/43f4b16a228c9286860a8167.png"},{"id":79765749,"identity":"ca58d285-af90-428d-b223-ed9ef6f5fb38","added_by":"auto","created_at":"2025-04-02 12:11:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":590916,"visible":true,"origin":"","legend":"\u003cp\u003eThe architecture of the proposed ensemble frameworks for inter-surgery image classification.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6338916/v1/d6e68c2c963590ccf7a1e708.png"},{"id":79765748,"identity":"86dbc339-abc5-421e-9152-1757746ca4bc","added_by":"auto","created_at":"2025-04-02 12:11:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162949,"visible":true,"origin":"","legend":"\u003cp\u003eThe architectures of the proposed ovary and uterus semantic segmentation frameworks.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6338916/v1/8a17953d8d9360de937f0862.png"},{"id":79767082,"identity":"262a012d-39d5-4d00-a2cd-2718f64251d4","added_by":"auto","created_at":"2025-04-02 12:35:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2430501,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6338916/v1/e120c93d-1c1b-4289-88dd-ee2d7507415e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAn Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAI, a pivotal subdiscipline of computer science, involves the development of systems capable of simulating human cognitive functions, including learning, reasoning, problem-solving, and decision-making [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Within the realm of surgeries and medical procedures, AI has emerged as a transformative innovation, driving advancements in medical analysis, diagnosis, drug discovery, personalized treatment recommendations, and autonomous surgical operations [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. AI-powered systems offer significant advantages, such as superior accuracy even under high-pressure conditions, immunity to fatigue, and reduced energy consumption, which collectively translate into economic benefits for patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Their unparalleled reliability is attributed to the ability to process vast and complex datasets, leveraging rigorous engineering design principles that exceed the limitations inherent to human expertise. Consequently, AI has been increasingly integrated into a wide array of medical processes, from diagnostic procedures and drug development to surgical assistance, heralding a new era of precision and efficiency in healthcare [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe integration of robotics and computerization in surgical procedures brings numerous advantages. Robotic systems surpass human capabilities by performing surgeries with unparalleled precision, enhanced speed, and reduced energy requirements. Remarkably, these systems can execute parallel surgical operations on multiple areas of a patient\u0026rsquo;s body, significantly expediting procedures while maintaining exceptional accuracy\u0026mdash;achievements that remain unattainable by human surgeons. As a result, the computerization of medical interventions has emerged as a rapidly advancing field of research and innovation, poised to transform the landscape of modern healthcare [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe automation of surgical procedures necessitates the application of AI methodologies, enabling computers to undertake medical and surgical tasks traditionally performed by human experts. To achieve this, AI systems must acquire the capability to learn from medical information, forming the basis for effectively executing surgeries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Learning, a complex process observed in humans and animals, is extensively studied within the fields of neuroscience, biology, and AI. Research highlights that learning occurs within the human brain, a sophisticated network of interconnected neurons. Deep learning, a specialized branch of AI, focuses on the development of software systems that emulate the neurons and the neural architecture of the human brain. As a result, these systems are expected to exhibit learning capabilities analogous to those of the human brain. This approach, grounded in the simulation of neural structures within a software environment, is commonly referred to as Artificial Neural Networks (ANNs) [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConvolutional Neural Networks (CNNs), a specialized subset of ANNs, are inspired by the neural mechanisms of the human visual system. These networks employ convolutional and pooling layers that emulate the processes of human vision, enabling the interpretation and processing of visual data such as images and videos. CNNs are widely applied in tasks such as classification, semantic segmentation, and localization. For classification tasks, CNNs typically begin with a series of convolutional layers designed to extract features from input data by identifying patterns such as edges, textures, and shapes. These are followed by fully connected layers, which integrate the extracted features and perform the final classification. This hierarchical architecture allows CNNs to transform raw visual data into meaningful categories, which can be used for decision-making procedures [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eU-Net models, an advanced subclass of CNNs, are distinguished by their U-shaped architecture, optimized for semantic segmentation and localization tasks. These tasks involve identifying the position of entities within an image, ranging from individuals depicted in images to medical conditions like cancerous cells or anatomical structures such as the uterus in robotic surgeries. U-Net models process input images through an encoder that extracts visual features, such as edges, textures, and colors. The extracted features are subsequently passed to a decoder, which performs pixel-level classification to generate a mask-like representation highlighting the location of the target entity within the original image [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Due to their robust performance, U-Net models are extensively employed in medical imaging applications, especially in scenarios requiring semantic segmentation and precise localization [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTransfer learning, a pivotal concept in deep learning, refers to the practice of applying pre-trained models\u0026mdash;typically trained on large-scale datasets\u0026mdash;to new tasks with smaller datasets. By leveraging the knowledge learned from a broader domain, transfer learning significantly reduces the time and computational resources required for training while improving accuracy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In medical applications, transfer learning enables the adaptation of CNNs and U-Net models to specific tasks, such as detecting pathologies or segmenting anatomical structures, with limited training data[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consequently, many studies, including the present work, utilize pretrained neural networks that have been previously trained on the ImageNet dataset, which comprises over 14\u0026nbsp;million images.\u003c/p\u003e \u003cp\u003eThis paper presents a reliable method for automating the movement and maneuvering of robotic arms and cameras in endometriosis robotic surgery. This method is relevant for AI-assisted surgeries and is vital for fully automated procedures, as described in [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To achieve this, the phases of a typical endometriosis surgical process are modeled as a decision-tree framework, in which nodes represent decision points or actions. Each decision-making node incorporates a reliable, accurate, and interpretable ensemble neural network classifier to assess the surgical state, identify the appropriate action, and proceed to the next step in the surgery. Here, \"action\" refers to the movement of robotic arms within the patient\u0026rsquo;s body.\u003c/p\u003e \u003cp\u003eThe proposed decision tree is designed to detect and address noise, such as obstructions on the camera lens caused by unexpected debris, including blood or water vapor. Additionally, it is structured according to the anatomical layout of the patient's body: the root node represents the robotic camera's entry point, while deeper nodes correspond to progressively deeper anatomical regions. Consequently, the proposed decision tree serves as an interpretable and adaptable model of surgical procedures. Interpretability is emphasized, as each processing unit's behavior is fully explainable. By segmenting surgical steps into small atomic phases and integrating a robust ensemble neural network framework for each phase, the proposed method optimizes both interpretability and reliability.\u003c/p\u003e \u003cp\u003eThe proposed ensemble frameworks enhance both the accuracy and reliability of the system. Assigning the same task to different neural networks with varying architectures enables the identification of similar patterns while capturing distinct noise. Integrating these networks into an ensemble framework allows for error correction, as the minimal overlap in individual neural network errors ensures overall higher robustness [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our findings also demonstrate that the ensemble neural network significantly improves system accuracy. Consequently, the proposed decision tree, which guides the robotic surgeon through endometriosis surgery, combined with the ensemble neural network frameworks at each decision-making node, establishes a reliable and robust system.\u003c/p\u003e \u003cp\u003eEndometriosis, the condition forming the basis for the surgeries discussed in this paper, is a prevalent health issue among women. It occurs when tissue similar to the lining of the uterus grows outside the uterine cavity, often leading to chronic pain and other complications. This condition commonly affects the pelvic region, including the ovaries, uterus, and peritoneum. Endometriosis can cause severe pain, particularly during menstruation, as well as infertility, fatigue, and other complications that significantly impact the quality of life. Treatment approaches vary depending on the severity of the disease, ranging from medication to alleviate symptoms, to surgical interventions aimed at removing endometrial tissue and restoring normal organ function[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Robotic surgery, as discussed in this paper, represents a cutting-edge approach that offers precision and adaptability in treating complex cases of endometriosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContribution of this paper are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDecision-Making Tree for Surgical Guidance\u003c/b\u003e: Design and development of a reliable decision-tree model to guide the robotic surgeon through the endometriosis surgery and automating its arm maneuvering and movements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFour Ensemble Neural Network Frameworks for State Detection\u003c/b\u003e: Design and implementation of four ensemble neural network frameworks for classification to analyze the surgical state, make decisions, and conduct the robotic surgery.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTwo Ensemble Semantic Segmentation and Localization Frameworks\u003c/b\u003e: Design and development of accurate and reliable ensemble frameworks for semantic segmentation and localization, enabling detection and positioning of the uterus and ovaries within real-time images during robotic endometriosis surgery.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis paper is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the existing literature, situating the proposed research within the context of current studies. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a detailed explanation of the proposed methodology. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e evaluates the proposed method through rigorous assessments. Finally, Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses the findings and proposes directions for future research.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eRecent research has extensively investigated the automation of medical procedures, with significant emphasis on the analysis and diagnosis of diseases using advanced computational techniques. For instance, deep learning methods have been employed to process mammographic images for breast cancer detection and diagnosis, as explored in studies [\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similarly, motion disorders such as Parkinson's disease have been a focal point of automation research, as detailed in [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Another study addresses the analysis of human motion to determine whether it is normal or pathological, emphasizing the importance of interpretability in intelligent medical applications [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This focus on interpretability underscores the necessity for intuitive algorithms that enhance system reliability, while also facilitating maintenance and future reconfigurations.\u003c/p\u003e \u003cp\u003eProgress in automation spans diverse medical fields, including oncology, motion disorders, psychological disorders, and infectious diseases. However, despite the advancements in these domains, research on automating surgical procedures remains comparatively limited. The automation and computerization of surgical processes, as this paper examines, offer the potential to significantly improve both medical and clinical outcomes, paving the way for more precise and efficient healthcare solutions.\u003c/p\u003e \u003cp\u003eThe integration of surgical procedures with AI and deep learning methodologies holds immense potential to enhance surgical accuracy, efficiency, and reliability. Nezhat et al. introduced the concept of utilizing AI applications in surgical practices, highlighting their ability to significantly advance medical and surgical processes. This integration also offers the prospect of standardizing surgical procedures by reducing variability in techniques employed by different surgeons for the same operation, thereby promoting consistency in surgical practices. Despite the promising outlook, Nezhat et al. did not propose a concrete methodology for achieving surgical automation through AI approaches [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have explored the automation of pathology detection during surgeries, leading to notable advancements. For instance, the automation of endometriosis detection during laparoscopic surgeries has been demonstrated in [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Another relevant approach is discussed in [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, despite these developments, the full computerization of surgical procedures remains unattained, as surgeons continue to perform critical tasks. Consequently, there is substantial scope for improvement in areas such as accuracy, cost-efficiency, procedural speed, bleeding control, and infection risk mitigation during surgeries, which can be promoted by performing a fully-automatic surgery.\u003c/p\u003e \u003cp\u003eOur previous work, detailed in [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], introduced a reliable method for the full automation of robotic surgeries, with a focus on endometriosis surgery. This study implemented the automation of detection, semantic segmentation, and localization of endometriotic lesions through a dependable and interpretable computer vision framework. Furthermore, an algorithm for fully automating the surgical process was proposed. However, the maneuvering of robotic cameras and arms was designed based on geometric data derived from the anatomical structures of the patient's body. To further advance the intelligent navigation of robotic arms, there is a need for a methodology that guides the robot through the sequence of surgical steps. This phase can be significantly improved by developing more robust frameworks that leverage advanced ensemble neural network techniques.\u003c/p\u003e \u003cp\u003eFrom the perspective of robotic maneuvering and arm movement, an interpretable method for the automatic control of robotic arms during surgery is proposed in [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This work introduces the Sina Tree, a framework designed based on the anatomical structure of human organs. However, further research is needed to explore the effective integration of computer vision techniques into the Sina Tree framework. Building upon this foundation, the current study presents a reliable decision-tree model enhanced by advanced ensemble neural network frameworks. The proposed model is designed to analyze the surgical context at each step, make informed decisions, and systematically guide the procedure during surgery.\u003c/p\u003e"},{"header":"3. Proposed Methodology","content":"\u003cp\u003eThis section outlines the proposed method for automating the maneuvering of robotic arms within the patient\u0026rsquo;s body in an endometriosis surgery. A decision-tree model is introduced, designed specifically to guide robotic systems step-by-step through typical endometriosis surgery procedures. The proposed tree comprises decision-making nodes, where data from the camera and other metadata inputs are analyzed to determine appropriate actions, such as proceeding with the surgery or halting it in response to an identified error. Additionally, the tree includes action nodes that dictate specific tasks, such as robotic arm and camera movements. Furthermore, advanced ensemble neural network frameworks have been developed and integrated into the decision-making nodes to enhance the system\u0026rsquo;s analytical and operational capabilities.\u003c/p\u003e \u003cp\u003eIn the first step of the proposed decision-tree, the system evaluates whether the camera captures a high-quality image. In certain cases, visual obstructions, such as blood particles or water vapor, can severely impair the robot's vision by introducing noise into the images. Such noisy images can lead to significant deviations from the robot\u0026rsquo;s intended operational strategy. Therefore, as a preliminary measure, the robot must assess the quality of the visual data received from the camera. If the image is excessively noisy, the system should notify clinical staff to address the issue. Alternatively, the robot can be equipped with mechanisms to autonomously clean the camera\u0026rsquo;s lens, thereby reducing reliance on clinical personnel and facilitating the surgical process.\u003c/p\u003e \u003cp\u003eIn some patients, adhesions may be present, posing obstacles during surgery. To address this, surgeons typically use a CO2 laser to vaporize the adhesions, thereby clearing the surgical field and facilitating the operation. Accordingly, in the second step of the proposed decision-tree, following confirmation that the images are free of noise, the robot must determine whether adhesions are present within the patient\u0026rsquo;s body. If adhesions are detected, the robot should employ the CO2 laser instrument to eliminate them and streamline the surgical process. Conversely, if no adhesions are found, the robot should proceed to the third step, wherein it determines whether to examine gynecological positions (the uterus and ovaries) or the peritoneum.\u003c/p\u003e \u003cp\u003eIn the third step of the proposed method, the robot confirms that the visual data is noise-free and that no adhesions are present within the body. Following this, the robot proceeds to survey the ovaries, uterus, or peritoneum to identify potential ovarian, uterine, or peritoneal endometriosis lesions. To achieve this, the robotic camera must rotate toward the upper region (the peritoneum) or the uterus. After performing this rotation, the system verifies whether the peritoneum or uterus is visible through the camera in order to certify the rotation. This process mimics the cognitive approach of laparoscopic surgeons, who rotate the laparoscope and observe the monitor to determine whether the peritoneum or uterus is within the field of view.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, there is a minimal risk of rotation failure during this step. If such an issue arises, the system should alert nurses and clinical experts to intervene and resolve the problem. While the probability of rotation failure is minimal, it must still be considered, given the critical importance of achieving fully autonomous surgery. Addressing even low-probability risks ensures maximum reliability of the system.\u003c/p\u003e \u003cp\u003eIf the peritoneum is selected for investigation, the robot must systematically survey its various regions to identify potential endometriosis lesions. Upon detecting such lesions, the robot can utilize a CO2 laser to effectively vaporize them. Using a CO2 laser is considered effective for eliminating pathological tissue and offers distinct advantages over traditional surgical approaches [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlternatively, if the uterus or ovaries are chosen for examination, the robotic arms must advance toward the uterus. At this stage, the robot is required to detect, semantically segment, and localize the uterus. To achieve this, a uterus semantic segmentation framework is employed. This framework not only enables the robot to maneuver its arms with precision but also assists in calculating the proximity of the robotic arms and camera to the uterus. Specifically, a larger segmented surface area of the uterus indicates a closer proximity of the camera to the uterus. To ensure safe movement of the camera toward the uterus, the distance between the uterus and the camera must be accurately calculated. This calculation is crucial for guiding the camera forward while preventing any potential collision with the uterus or surrounding tissues.\u003c/p\u003e \u003cp\u003eAfter reaching the uterus, it must be held and stabilized to allow for meticulous and careful examination. We propose the use of suction instruments instead of traditional grasping tools, as suction instruments not only simplify surgical procedures but also minimize potential damage to the uterine tissue. To achieve this, the uterus semantic segmentation framework is essential. This framework facilitates the process of stabilizing the uterus for the investigation of uterine endometriosis and assists in positioning and holding the uterus to access the ovaries for the identification of ovarian endometriosis.\u003c/p\u003e \u003cp\u003eFor cases of ovarian endometriosis, a similar strategy should be employed following the stabilization of the ovaries using either grasping instruments or suction instruments. Subsequently, the ovary must be carefully detected and localized. To achieve this, an ovary semantic segmentation framework is required, enabling the simultaneous detection and localization of both the ovary and uterus for the purpose of identifying ovarian endometriosis. Finally, upon accurately detecting, localizing, and segmenting endometriosis lesions\u0026mdash;whether on the peritoneum, uterus, or ovaries\u0026mdash;the robot can utilize its CO2 laser to vaporize the pathological tissue. The proposed decision-tree model is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For the endometriosis lesions detection and localization, the ensemble framework introduced in [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] can be used.\u003c/p\u003e \u003cp\u003eIn the proposed decision-tree framework, four decision-making nodes are designed to address noise detection, adhesion detection, peritoneum or uterus classification, and ovary detection, respectively. For each decision-making node, an advanced ensemble neural network framework has been developed to perform binary classification. Additionally, two semantic segmentation and localization frameworks are implemented to identify the uterus and ovaries during surgery, which are essential for determining organ positions and enabling appropriate surgical actions.\u003c/p\u003e \u003cp\u003eThe ensemble frameworks enhance the reliability of the proposed system by integrating multiple AI models, thereby yielding more precise decisions. This approach can be compared to a scenario where a panel of experts deliberates on a problem, resulting in more reliable and trustworthy outcomes. Furthermore, as the neural networks within the ensemble framework operate in parallel, there is no significant impediment to the overall execution speed.\u003c/p\u003e \u003cp\u003eThe structures of the proposed ensemble neural network classifiers are designed with an initial layer that extracts visual features from the input image. This layer employs various architectures, such as ResNet and VGG, among others. The extracted features are then passed to a fully connected layer responsible for determining the class to which the input image belongs. Based on this classification, the network generates a vote.\u003c/p\u003e \u003cp\u003eSimultaneously, additional neural networks employing different architectures process the input image, each producing its own votes. Finally, a collective decision is made by aggregating the votes from the individual networks. This final decision guides the robot through the decision-making nodes in the proposed decision-tree model. The structure of the ensemble neural network classifiers, incorporating diverse architectures as feature extractors, is illustrated in Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eThe architecture of the proposed ensemble neural network classifier, which is designed to determine whether the input image is noisy or of high quality, is illustrated in Fig.\u0026nbsp;2.a. In each neural network, the image or frame from the real-time video is initially processed by a feature extractor layer. This layer can be implemented using various CNN architectures, such as ResNet, VGG, and others. The extracted visual features are subsequently passed to a fully connected layer (FCL), which determines the category to which the input image belongs. This methodology is uniformly applied across all proposed ensemble classification frameworks. However, the frameworks differ in the number of feature extractor layers and the specific architectures utilized.\u003c/p\u003e \u003cp\u003eTo design the ensemble neural network frameworks, a systematic approach was employed. First, various architectures were examined for each classification task. Subsequently, the outputs of all neural networks for the same test set of images were recorded. Finally, a search algorithm was utilized to evaluate all possible designs for the ensemble framework. The ensemble with the highest accuracy was ultimately selected for inclusion in the proposed system. In cases where multiple ensemble frameworks achieved the same accuracy, the framework with a greater number of networks was chosen to enhance the system's reliability. Since the output of the ensemble framework is generated by a larger number of advanced networks, the results are anticipated to be more robust and dependable.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that the ensemble neural network framework, incorporating feature extractor architectures such as ResNet50, ResNet50V2, VGG16, VGG19, ResNet101, ResNet101V2, InceptionResNetV2, DenseNet169, DenseNet201, and NASNetLarge, achieved the highest reliability for the first decision-making node of the proposed tree. This node is responsible for classifying noisy images from high-quality ones. In this design, the threshold value, set to 2, determines that if the number of votes within an ensemble framework meets or exceeds this value, the classifier produces a positive output. For the first ensemble framework, a positive output corresponds to the noisy image class. The architecture of this framework is depicted in Fig.\u0026nbsp;2.a.\u003c/p\u003e \u003cp\u003eSimilarly, the proposed ensemble framework for the second decision-making node in the decision tree comprises 14 independent neural networks employing various feature extractor architectures, including InceptionV3, MobileNet, ResNet50, ResNet50V2, VGG16, VGG19, ResNet101, ResNet101V2, InceptionResNetV2, DenseNet121, DenseNet169, DenseNet201, NASNetLarge, and ConvNeXtXLarge. The threshold value for the second ensemble framework is set to 10. This framework is designed to classify images containing adhesions from normal images, with its architecture illustrated in Fig.\u0026nbsp;2.b.\u003c/p\u003e \u003cp\u003eFor the third decision-making node in the proposed tree, an ensemble framework was developed to classify whether the input image contains the uterus or peritoneum. Our findings indicate that the ensemble framework incorporating feature extractor layers with architectures such as InceptionV3, ResNet50, ResNet50V2, VGG16, VGG19, ResNet101, ResNet101V2, InceptionResNetV2, DenseNet169, DenseNet201, MobileNet, and NASNetLarge demonstrates sufficient reliability. The threshold value for this ensemble framework is set to 1. The architecture of this framework is illustrated in Fig.\u0026nbsp;2.c.\u003c/p\u003e \u003cp\u003eSimilarly, for the fourth decision-making node in the proposed tree, the ensemble classifier utilizes InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, InceptionResNetV2, VGG16, VGG19, MobileNet, DenseNet121, DenseNet169, ConvNeXtXLarge, and NASNetLarge as the feature extractor architecture. The threshold value for this framework is set to 10. The architecture of the proposed ensemble framework for the fourth decision-making node is depicted in Fig.\u0026nbsp;2.d.\u003c/p\u003e \u003cp\u003eIn the proposed decision tree framework, semantic segmentation and localization tasks are handled using two ensemble frameworks\u0026mdash;one dedicated to uterus detection and the other to ovary detection. Each ensemble framework integrates three U-Net models, employing ResNet50, ResNet101, and VGG19 architectures as backbone networks. Within the decision tree, the segmentation and localization frameworks for the uterus and ovary are executed following classification at the third and fourth decision-making nodes.\u003c/p\u003e\n\u003cp\u003eWhile ovary detection can be performed without a classification framework, this approach has been designed to minimize noise-related risks, thereby improving the overall reliability of the system. The architectures of the proposed semantic segmentation frameworks for uterus and ovary detection are depicted in Figs. 3.a and 3.b, respectively.\u003c/p\u003e\n\u003cp\u003eIn ensemble frameworks for classification, each neural network generates a binary decision that identifies the category to which the input image belongs. Similarly, in semantic segmentation frameworks, each neural network produces binary decisions for individual pixels within the input image. To achieve accurate semantic segmentation and localization of the uterus and ovary during robotic and laparoscopic surgery, the votes from all neural networks are aggregated for each pixel.\u003c/p\u003e\n\u003cp\u003eAs detailed in Section \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, our findings reveal that the proposed neural network frameworks for both classification and semantic segmentation tasks exhibit robustness and reliability. Moreover, the decision tree, developed based on medical methodologies for endometriosis surgery and integrated with advanced deep learning frameworks, enhances system reliability. The interpretability of the decision tree further supports its adaptability and ease of maintenance for future applications.\u003c/p\u003e\n\u003cp\u003eBy presenting a dependable approach for maneuvering robotic arms within a patient\u0026apos;s body during surgery, the proposed method significantly improves the efficiency and precision of surgeries. When combined with advanced detection techniques, as referenced in [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e], this approach enables fully automated surgical procedures powered by reliable AI systems. This innovation not only enhances surgical accuracy and reliability but also reduces the risks of post-operative infections, decreases economic costs, and mitigates intraoperative bleeding.\u003c/p\u003e"},{"header":"4. Evaluation","content":"\u003cp\u003eIn this section, the proposed method is validated through a comprehensive evaluation of all modules within the proposed ensemble framework. The ensemble architecture not only enhances the system\u0026rsquo;s accuracy but also improves its reliability. This is because different neural networks with varying architectures, when trained on the same dataset, tend to identify consistent patterns while learning different noises. Consequently, the ensemble framework mitigates unexpected noise and errors by employing a consensus-based approach.\u003c/p\u003e\n\u003cp\u003eThe initial step of the system occurs at the beginning of the surgical procedure, where it verifies whether the camera is functioning correctly or obstructed by elements such as debris or blood. For this phase, multiple deep neural networks with diverse architectures are developed. These networks are integrated into an ensemble framework to construct a system capable of determining whether the input image depicts the internal body, intense tissue adhesions that impair visibility, or camera malfunctions producing severe noise (e.g., fully white, fully dark, or completely blurred screens). If the camera correctly captures the internal body, the robotic system advances the camera to explore deeper regions. In cases where intense adhesions are detected, the robot employs a CO2 laser to vaporize the tissue and proceed with the surgery. For severe noise, the system alerts medical staff to address the issue manually.\u003c/p\u003e\n\u003cp\u003eIn this study, scenarios involving severe noise in camera imagery are classified as abnormal states, while tissue adhesions or normal internal body conditions are categorized as normal states. A binary classification approach is developed using deep neural networks to distinguish between these conditions. Finally, a full binary tree is employed to identify the optimal ensemble architecture, utilizing networks such as ResNet50. An image is classified as abnormal only if two or more networks within the ensemble detect it as abnormal.\u003c/p\u003e\n\u003cp\u003eThe evaluation results of the ensemble neural network framework for the initial phase of the surgical procedure are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. As demonstrated, the ensemble network achieves the highest accuracy compared to individual neural networks. These findings indicate that the ensemble framework effectively minimizes errors while maximizing both accuracy and reliability. Neural networks with lower accuracy were excluded from the ensemble framework to ensure optimal performance. Using the individual neural networks for the proposed ensemble framework is reported as \u0026lsquo;Ensemble Module\u0026rsquo; in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cspan style='font-size:13px;line-height:115%;font-family:\"Cambria\",serif;'\u003eTable 1, The evaluation results for the neural network framework for the first step of the surgery.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse;border: none;width: 638px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border: 1pt solid windowtext;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eArchitecture\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTrain Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eValidation Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTest Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003ePrecision\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eRecall\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eF1-Score\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eSpecificity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEnsemble Module\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.07\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.97\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.34\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50V2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e69.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eVGG19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eVGG16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet101\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.62\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e81.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e89.71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet101V2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eInceptionV3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid 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54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e52.23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e66.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.87\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e66.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e49.70\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e66.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eConvNeXtXLarge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e87.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e80.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e86.33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64.55pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eProposed Ensemble Framework\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.67\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.65in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.2pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eSimilarly, for the ensemble frameworks proposed for the second, third, and fourth steps of the decision tree, evaluation processes were designed, demonstrating that the frameworks are both accurate and reliable. In each framework, the final decision is generated through the parallel execution of multiple diverse neural networks, thereby maximizing system reliability. The results for the proposed ensemble framework, which classifies input images as containing adhesions or being adhesion-free, are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cspan style='font-size:13px;line-height:115%;font-family:\"Cambria\",serif;'\u003e\u003cbr\u003eTable 2, The evaluation results for the neural network framework for the second step of the surgery.\u0026nbsp;\u003c/span\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003ctable style=\"border-collapse: collapse;border: none;width: 639px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border: 1pt solid windowtext;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eArchitecture\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTrain Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eValidation Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTest Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003ePrecision\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eRecall\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eF1-Score\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eSpecificity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEnsemble Module\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e83.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e91.22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e79.73\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e84.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e84.29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e91.48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e80.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e87.03\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.06\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e84.31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.73\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e83.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e91.22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e79.74\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e74.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e84.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e76.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e86.79\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.97\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan 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style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e70.38\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e63.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e77.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e39.22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eDenseNet121\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.90\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e89.54\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eDenseNet169\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e80.25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e72.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e83.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e59.48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eDenseNet201\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e82.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 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54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e33.33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan 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style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 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54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e83.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e53.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.79\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.79\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid 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54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e48.15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.79\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.79\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eConvNeXtXLarge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e87.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e89.49\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e82.99\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.70\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e78.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eProposed Ensemble Framework\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3, The evaluation results for the neural network framework for the third step of the surgery\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable style=\"border-collapse: collapse;border: none;width: 639px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border: 1pt solid windowtext;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eArchitecture\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTrain Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eValidation Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTest Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003ePrecision\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eRecall\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eF1-Score\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eSpecificity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEnsemble Module\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50V2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e68.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e37.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e54.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eVGG19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e60.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e20.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e33.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eVGG16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e89.63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e64.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e28.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e43.75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet101\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet101V2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e70.87\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e70.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e75.95\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e60.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.04\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e81.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e53.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e52.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eConvNeXtXLarge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.86\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e60.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e20.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e33.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eProposed Ensemble Framework\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;line-height:115%;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eFor the third step of the proposed decision tree model, an ensemble framework was developed and evaluated, demonstrating its ability to accurately and reliably classify input images as either peritoneum or uterus images. The evaluation results for this framework are presented in Table 3. Similarly, the evaluation details for the fourth ensemble framework, which classifies images as depicting a uterus with or without an ovary, are provided in Table 4.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003eTable 4, The evaluation results for the neural network framework for the fourth step of the surgery.\u003c/div\u003e\n\u003ctable style=\"border-collapse: collapse;border: none;width: 639px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border: 1pt solid windowtext;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eArchitecture\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTrain Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eValidation Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTest Accuracy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003ePrecision\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eRecall\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eF1-Score\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eSpecificity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEnsemble Module\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.04\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50V2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e78.90\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e72.86\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.73\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e81.60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e64.81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eVGG19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e75.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e79.82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e72.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e83.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e61.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eVGG16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e76.09\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e75.23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet101\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e88.99\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e83.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e90.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e79.63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet101V2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.73\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eInceptionV3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e96.30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eInceptionResNetV2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e82.78\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: 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rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e88.99\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt 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style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e88.88\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eNASNetLarge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.76\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e87.16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e80.60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e88.52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan 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style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e46.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.07\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp 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rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e51.35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt 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rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e48.65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.07\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEfficientNetB7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e52.55\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e54.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e50.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e67.07\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e0.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(229, 184, 183);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026times;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eConvNeXtXLarge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e84.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e88.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e80.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e83.81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e88.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(214, 227, 188);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style=\"font-size:13px;font-family:Wingdings;color:black;\"\u003e\u0026uuml;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.3in;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eProposed Ensemble Framework\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e---\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.9pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.35pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.85pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e100.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.8pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(182, 221, 232);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe evaluation results indicate that the proposed decision tree model, equipped with the ensemble frameworks, achieves high accuracy and reliability. Furthermore, as the neural networks within the frameworks operate in parallel, the system maintains computational efficiency. This rapid performance is crucial for conducting surgical procedures at the highest possible speed, minimizing risks of infection, bleeding, and post-operative pain.\u003c/p\u003e\n\u003cp\u003eThe proposed decision-tree model incorporates not only ensemble classifiers but also semantic segmentation and localization frameworks for detecting the uterus and ovaries during surgery. To achieve this, U-Net models were developed to identify the uterus and ovaries effectively. Subsequently, two ensemble frameworks were designed for detecting the uterus and ovaries, respectively. Each framework demonstrates accuracy, reliability, and interpretability, as they are designed to detect simple patterns. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e details the evaluation metrics for the proposed semantic segmentation frameworks. This approach contrasts with some studies where a single neural network is tasked with identifying multiple concepts. Consequently, the overall system, proposed to automate the robotic maneuvering of arms, cameras, and instruments within the patient\u0026rsquo;s body during endometriosis surgery, is highly reliable.\u003c/p\u003e\n\u003cp\u003eIntersection over Union (IoU) is a metric commonly used to assess the accuracy of semantic segmentation tasks by measuring the overlap between predicted segmentation and ground truth. It is calculated as the ratio of the intersection area to the union area of the predicted and ground truth segments, capturing both false positives and false negatives. IoU provides a quantitative evaluation of segmentation performance, with higher values indicating better alignment between predictions and actual segments. In this study, IoU is used to evaluate the effectiveness of the proposed semantic segmentation frameworks for detecting the uterus and ovaries, with the results presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e to demonstrate the system\u0026apos;s precision in localization and segmentation during surgery.\u003c/p\u003e\n\u003cp\u003eTable 5, The evaluation results for the proposed uterus and ovary semantic segmentation and localization frameworks.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"center\" style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\n \u003ctable style=\"border-collapse: collapse; border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"border: 1pt solid windowtext;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eU-Net backbone\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026nbsp;architecture\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eUterus Localization\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eOvary Localization\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTrain\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026nbsp;IoU\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eValidation\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026nbsp;IoU\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTest\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eIoU\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTrain\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026nbsp;IoU\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eValidation\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e\u0026nbsp;IoU\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eTest\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eIoU\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.77\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eResNet101\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp 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windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eVGG19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e99.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e95.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.42\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e91.88\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e94.55\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;background: rgb(191, 191, 191);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eProposed\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eEnsemble\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003eFramework\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e98.16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(194, 214, 155);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e97.98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e92.98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(146, 205, 220);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;font-family:\"Cambria\",serif;color:black;'\u003e93.64\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003eBy integrating this methodology with other techniques for fully automated surgeries, surgical and medical procedures can be performed with greater accuracy, faster execution, reduced economic costs for patients and clinical centers, lower risks of postoperative infections and pain, minimized bleeding, and faster recovery. Furthermore, the proposed method is adaptable to the automation of other surgical operations, such as cancer-related procedures. Since the proposed robotic surgeons are based on knowledge-based programs, and given that such knowledge can be easily enhanced, transferred, and replicated within computer systems, this methodology has the potential to significantly increase access to medical and clinical facilities\u0026mdash;even in regions with acute shortages of medical devices, specialists, and experienced surgeons. All that is required for surgery is a robot connected to the Internet, which can be easily accessible in many parts of the world.\u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Discussion and Future Works","content":"\u003cp\u003eIn this paper, an intelligent and reliable method is proposed to automate robotic maneuvering during robotic endometriosis surgeries. The proposed approach enables fully automated surgical procedures, offering several advantages, including reduced bleeding, accelerated operation times, minimized risk of infection, diminished postoperative pain due to shorter surgery durations, decreased economic costs, and improved surgical accuracy.\u003c/p\u003e \u003cp\u003eThe methodology involves dividing the surgical process into atomic phases, each analyzed using an ensemble neural network framework. This framework is designed to detect the status of each step and determine the appropriate subsequent actions. To enhance the reliability of the approach, rare challenges, such as camera noise and adhesions within the patient\u0026rsquo;s body, are considered. Additionally, a binary decision tree is introduced to enable fully automated maneuvering of robotic arms during surgeries. This decision tree incorporates ensemble neural networks at each decision-making node, allowing the robotic arms to operate reliably based on the detected status of the surgery at every decision point.\u003c/p\u003e \u003cp\u003eIn this paper, endometriosis, a common condition affecting women, is used as the foundation for the surgical framework. Consequently, the proposed decision-making tree is specifically designed for ovarian or uterine endometriosis surgeries. However, the methodology can be extended to develop reliable decision-making trees for other types of surgeries, such as those for cancer treatment. This extension will be explored in future research.\u003c/p\u003e \u003cp\u003eWhile this paper proposes an intelligent and reliable framework to guide robotic surgeons during endometriosis surgeries, further advancements can be achieved by exploring additional computing techniques, such as cloud computing. A cloud-based strategy offers advantages such as more efficient computation and simple centralized updates. However, implementing this strategy necessitates the design and development of secure data stream channels. To maximize the effectiveness of the proposed method, future work will also focus on enhancing the hardware responsible for executing the computational processes.\u003c/p\u003e \u003cp\u003eIn addition, while valuable datasets for gynecological research currently exist, the proposed deep learning systems can be strengthened by incorporating larger and more diverse datasets. These could include additional laparoscopic and robotic surgery videos and images. Providing such data for research and development purposes is another critical objective planned for future investigations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWinston PH (1992) Artificial intelligence. 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JSLS: J Soc Laparosc Robotic Surg 27(1):e2022\u0026ndash;00073\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaidi SA, Chouvatut V, Phongnarisorn C, Praserttitipong D (2025) Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation. Intelligence-Based Med, 100230\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacis, C., Santoro, M., Zybin, V., Di Costanzo, S., Coada, C. A., Dondi, G., \u0026hellip; Strigari,L. (2025). A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Related Ovarian Cancer. Applied Sciences, 15(6), 3070.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaadati S (2023) Nahid: AI-based Algorithm for operating fully-automatic surgery. arXiv preprint. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.2401.08584\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2401.08584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. arXiv:2401.08584\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fully-automated robotic surgery, Artificial Intelligence in medicine, Computer Vision in robotic surgery, Detection and Segmentation for medical imaging, medical image processing","lastPublishedDoi":"10.21203/rs.3.rs-6338916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6338916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) and computer vision are revolutionizing numerous fields, including robotic surgery, which stands to benefit immensely from advances in machine learning methodologies. While prior research has extensively focused on disorder detection, localization, and semantic segmentation, the crucial challenge of robotic arm maneuvering during autonomous surgeries remains underexplored. This study proposes a robust and interpretable approach to enable robots to autonomously execute endometriosis surgeries by skillfully navigating their arms, equipped with a camera and surgical tools such as graspers or lasers. A decision tree framework is developed to assess the robot's real-time status and guide its actions at every surgical stage. This framework integrates diverse ensemble neural network models for classification and semantic segmentation to support decision-making. Specifically, the proposed ensemble classification models utilize deep learning to assess image quality, identify obstructions caused by adhesions, detect anatomical targets (e.g., uterus or peritoneum), and determine the proximity of the ovary to the uterus. The proposed ensemble semantic segmentation models further enhance accuracy by detecting and localizing the uterus and ovary. By employing these ensemble frameworks within the proposed decision tree model, this work aims to advance robotic surgery capabilities, enabling fully autonomous, reliable, and efficient operations. Consequently, the proposed method aims to minimize economic costs, bleeding, post-operative pain, and infection risk, while optimizing surgical precision and performance.\u003c/p\u003e","manuscriptTitle":"An Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 12:11:40","doi":"10.21203/rs.3.rs-6338916/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d0e8db0-0d48-4b75-8811-7829c9303a69","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46414465,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-04-02T12:11:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-02 12:11:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6338916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6338916","identity":"rs-6338916","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}