PolypSegNet: A Hybrid ConvNeXt-Tiny and Attention U-Net Framework for Accurate Colorectal Polyp Segmentation

PolypSegNet: A Hybrid ConvNeXt-Tiny and Attention U-Net Framework for Accurate Colorectal Polyp Segmentation | 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 PolypSegNet: A Hybrid ConvNeXt-Tiny and Attention U-Net Framework for Accurate Colorectal Polyp Segmentation Riduana Adneen adrita, Md Mahenur Islam, Mohammad Khaled Sohel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7102819/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 Colorectal cancer is still one of the most common causes of cancer deaths around the world. Finding polyps early is very important for preventing this type of cancer. However, it is very hard to automatically segment colorectal polyps in endoscopic images because they are not always the same shape, have low contrast, and have different levels of light. We present PolypSegNet in this paper. It is a new hybrid deep learning framework that combines the feature extraction abilities of ConvNeXt-Tiny with the spatial awareness and localization abilities of Attention U-Net. Our architecture combines a lightweight ConvNeXt encoder with attention-boosted skip connections and decoder blocks. This makes it possible to keep context and draw precise boundaries. We test our model on the Kvasir-SEG dataset, which is available to the public, and show that PolypSegNet does a better job of segmenting than traditional U-Net and other baseline models. Specifically, our method has a dice coefficient of 0.9420, an IoU of 0.8930, and an F1-score of 0.9415 which shows how well it works. The results show that combining hierarchical ConvNeXt features with attention mechanisms makes polyp detection much more accurate. This work points to a promising way to segment colorectal polyps in real time and with high accuracy in clinical practice. Bioinformatics Biochemical Research Methods Computational Biology Colorectal Polyp Segmentation Biomedical Image Analysis Hybrid Deep Learning Architecture ConvNeXt-Tiny Attention U-Net Kvasir-SEG Dataset Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Colorectal cancer (CRC) is one of the most common and life-threatening cancers in the global context. The prompt identification of polyps during colonoscopy is very important for stopping it [1] [2] . Automated segmentation of colorectal polyps in endoscopic images helps doctors by giving them quick and accurate outlines [3] . But this job is hard because polyps have strange shapes, don't stand out against adjacent tissue, and have artifacts [4] . Traditional convolutional neural networks (CNNs), especially the U-Net architecture, have been very important for medical segmentation CITATION Ron15 \l 1033 [5] . The encoder-decoder structure of U-Net, which includes skip connections, keeps the spatial context and has worked well on colonoscopy data. Still, these kinds of models have a hard time capturing both the big picture and minor details at the same time, especially in low-light scenes or with small polyps [6] . Recent progress combines CNN backbones with attention or positional embedding modules to fill in semantic gaps. For example, ConvNeXt-based models greatly improve the accuracy of segmentation by using better hierarchical representation CITATION Luh22 \l 1033 [7] . Hybrid networks that combine U-Net with transformers or attention gates also improve the segmentation of unclear areas CITATION Pan25 \l 1033 [8] . We suggest PolypSegNet, a hybrid framework that combines the global feature extraction of the ConvNeXt-Tiny encoder with the channel-spatial attention blocks of the Attention U-Net decoder. This design works well to segregate colorectal polyps into different textures and boundaries, making it perfect for use in a variety of imaging situations. We test our model on the Kvasir-SEG dataset and get a remarkable dice score of 0.9420, an IoU of 0.8930, and an F1-score of 0.9415, which outperforms both standard U-Net and other recent models. The results show that using modern CNN architectures with attention modules makes the system more accurate and stable, which is good for clinical use. 2. Related Work Automated polyp segmentation in colonoscopy images has attained a lot of attention because it is very important for diagnosis of colorectal cancer at an early stage. In the era of Artificial Intelligence, medical image segmentation has made efficient use of traditional deep learning architectures, especially encoder-decoder frameworks like U-Net [9].The first U-Net did very well on biomedical images by using skip connections to get spatial information back during upsampling. Chen Si surveyed on his paper about the limitations and scopes of U-Net based architecture for colorectal polyp segmentation [10], there was shown that U-Net has trouble modeling long-range dependencies. To fix this, attention mechanisms were introduced [11]. Attention U-Net enhances U-Net to focus on important parts of an image, which makes it better at segmenting small, complicated lesions like polyps. In the same way, U-Net++ used nested and dense skip pathways to make multi-scale feature fusion better [12]. Recent improvements use transformer-based modules to make global context modeling better. TransUNet [13] uses a Vision Transformer (ViT) decoder and CNN encoders to combine them. It uses the global self-attention mechanism to pick up on all the features at once [14]. These hybrid models are very powerful, but they are also very expensive to run and often need big datasets to train well [15] . At the same time, ConvNeXt [16], a new convolutional architecture, showed that CNNs can perform as well as transformers on vision tasks if their architecture is carefully tuned. It uses large kernel convolutions and layer normalization to stay efficient and simple, which makes it a great encoder for medical uses. Polyp-PVT [17], for instance, utilizes Pyramid Vision Transformers with multi-scale fusion but isn't lightweight in realization for real-time clinical use. PraNet [18] suggests an inverse attention mechanism but is more suitably used for binary segmentation and doesn't leverage modern CNNs like ConvNeXt. To overcome these limitations, our research proposes the PolypSegNet architecture as a combination of the strengths of ConvNeXt-Tiny (hierarchical representation richness) and Attention U-Net (spatial accuracy). Its dual architecture ensures both global context and accurate boundary delineation at low computational complexity. 3. Methodology Here, we present a comprehensive description of the hybrid architecture of our proposed model, PolypSegNet, for the precise segmentation of colorectal polyps. The process is divided into five main sections: preprocessing of the dataset, architectural framework, fusion methods, training process, and evaluation metrics. 3.1 Dataset and Data Preprocessing The Kvasir-SEG dataset [19] is an open-access colorectal cancer imaging collection that includes 1000 polyp images along with their corresponding pixel wise segmentation masks. Each image with masks was resized to 256×256 resolution and normalized to the [0,1][0, 1][0,1] scale. The data was divided into training, validation, and testing sets using a standard 70:20:10 ratio. Preprocessing Steps Resizing all images and masks to 256×256. Normalizing pixel intensities. Data augmentation: random flipping, rotation, and zooming to improve generalization. 3.2 Proposed Architecture: PolypSegNet From the Figure-1, we have shown the system flow of the PolypSegNet. The proposed PolypSegNet framework features a hybrid encoder-decoder architecture that combines ConvNeXt-Tiny as the encoder and Attention U-Net as the decoder. A Squeeze-and-Excitation (SE) fusion block is also included between the encoder and decoder to improve spatial channel-wise feature recalibration. Encoder: ConvNeXt-Tiny ConvNeXt-Tiny is a modern ResNet-like backbone that uses hierarchical convolutional blocks. It acts as a strong feature extractor and generates multi-level representations: Stage 1: 64×64×96 Stage 2: 32×32×192 Stage 3: 16×16×384 Stage 4 (Bottleneck): 8×8×768 These feature maps are fused through the decoder after being sent via skip connections. SE Fusion To improve discriminative capabilities, SE blocks are used to recalibrate the encoder outputs by modeling channel dependencies. This fusion boosts polyp-relevant features before up sampling. Decoder: Attention U-Net The decoder relies on Attention U-Net blocks, which use attention gating to focus on important encoder features during skip connections. The hierarchical decoding procedure includes: Stage 1: 8×8×768 → 16×16×512 Stage 2: 16×16×512 → 32×32×256 Stage 3: 32×32×256 → 64×64×128 Stage 4: 64×64×128 → 128×128×64 Stage 5: 128×128×64 → 256×256×32 A final 1×1 convolution + sigmoid produces the binary segmentation mask. 3.3 Training Details ● Loss Function : Combination of Binary Cross-Entropy (BCE) and Dice Loss was used to optimize boundary precision and region overlap. ● Optimizer : For optimization, we used Adam, where the initial learning rate was 0.001. And then gradually we adjusted it via a learning rate scheduler. ● Epochs : Trained for 25 epochs on Google Colab using an NVIDIA T4 GPU. ● Batch Size : 16 ● Callbacks : EarlyStopping and Model Checkpoint were used to save the best-performing model. 3.4 Evaluation Metrics To evaluate segmentation quality, the following metrics were computed for the hybrid framework: 3.5 Model Visualization To offer a clear understanding of the internal workings of the proposed PolypSegNet architecture, a schematic visualization is presented in Figure 2 . This illustration captures the complete end-to-end flow of information from input image to binary segmented output. At the left side , the input endoscopic image is first passed through the ConvNeXt-Tiny encoder , which hierarchically extracts spatial features at four progressive levels: ● Level 1: 64×64×96 ● Level 2: 32×32×192 ● Level 3: 16×16×384 ● Level 4: 8×8×768 (bottleneck) These features are efficiently encoded through convolutional stages that include residual and normalization layers [20]. The Squeeze-and-Excitation (SE) fusion block is crucial at this stage, as it learns to amend the feature channels by deciding which one is to emphasize or suppress [21]. This improves the encoder’s ability to focus on polyp-related features. Next, on the right side, the decoder begins a series of upsampling operations. Each decoding stage uses Attention U-Net blocks, which apply soft attention to selecting only the most relevant encoder features during skip connections [22] [23]. This method reduces background noise and improves segmentation accuracy for small or unclear polyps. ● The decoder first expands the bottleneck (8×8×768) back to full resolution through five stages. ● Attention gates at each level improve the combination of encoder-decoder features. ● A 1×1 convolution laye r followed by a sigmoid activation creates the final binary segmentation mask. At the far right of the diagram, the output shows a binary mask that highlights the polyp regions segmented from the input image. This visual architecture can provide a complete overview of the PolypSegNet pipeline. It showcased its hybrid fusion architecture, multi-scale feature handling and attention-enhanced skip connections. Together these elements help improve segmentation accuracy. 4. Experimental Results and Discussions This section provides a comprehensive evaluation of the proposed PolypSegNet framework . We present both quantitative and qualitative comparisons with existing baseline models (U-Net and Attention U-Net) to demonstrate the novelty of the hybrid architecture grounded in ConvNeXt-Tiny and Attention U-Net. Additionally training behavior and ablation insights are discussed to validate individual components of the framework. 4.1 Quantitative Evaluation We assess the segmentation performance with five standard metrics. These metrics are the Dice Coefficient, Intersection over Union (IoU), Precision, Recall, and F1 Score. We calculate all results using the validation set from the Kvasir-SEG dataset. Table 1 Comparison of PolypSegNet with baseline models Model Dice Coefficient IOU Precision Recall F1 Score U-Net (Baseline) 0.7541 0.6223 0.7793 0.7320 0.7550 Attention U-Net 0.8192 0.7084 0.8476 0.7955 0.8207 PolypSegNet 0.9420 0.8930 0.9381 0.9456 0.9415 As shown in table-1, PolypSegNet clearly performs better than both U-Net and Attention U-Net for all evaluation metrics. The model achieves a Dice Coefficient of 0.942 and an IoU of 0.893 , indicating a strong overlap between the predicted and ground-truth masks. 4.2 Training and Validation Behavior The training was conducted for 25 epochs using the Adam optimizer, where the scheduler was ReduceLROnPlateau. The training and validation performance metrics were recorded per epoch, showing smooth convergence and generalization. The model achieved its best performance at Epoch 18 , with: Training Accuracy : 98.00% Training Loss : 0.0276 Validation Accuracy : 96.26% Validation Loss : 0.0784 4.3 Qualitative Results We performed qualitative comparisons for further validation. Figure 4 shows the visual results from sample validation images. The comparison was held by original image with Ground Truth Mask vs Predicted Mask for - U-Net Attention U-Net PolypSegNet From Fig. 5 , PolypSegNet consistently yields sharper, more complete segmentations, especially for small and irregularly shaped polyps, where baseline models struggle. 4.4 Ablation Insights To evaluate the individual impact of each main component in the PolypSegNet architecture. So, we carried out an ablation study. We removed one module at a time - leaving the rest of the architecture unchanged. This method shows how much each module contributes to the model's overall performance. Table 2 Ablation insights of PolypSegNet Variant Dice Precision Recall Observations Full PolypSegNet 0.942 0.9471 0.9540 Best overall performance ConvNeXt Encoder 0.907 0.9122 0.9254 ~ 3.5% Dice drop; loss of rich features Attention Gates 0.918 0.9237 0.9310 Reduced ability to localize fine boundaries SE Fusion Module 0.923 0.9268 0.9401 ~ 2% Precision drop; unstable convergence From Table 2 , each of the aborted components resulted in a clear degradation in segmentation's quality, measured in Dice Score, Precision, and Recall. Key Takeaways : ConvNeXt Encoder plays a critical role in extracting deep semantic features that enhance segmentation accuracy. Attention Gates help localize polyps more precisely, especially in ambiguous or fine-boundary regions. SE Fusion facilitates better channel-wise feature recalibration, improving both learning stability and predictive precision. These findings show that the proposed improvements are not unnecessary. They work together to boost performance gradually. The ablation results support the idea that PolypSegNet’s design is both useful and well-founded. 5. Conclusion and Future Work 5.1 Summary of Findings This study presents PolypSegNet , a new hybrid deep learning framework that combines ConvNeXt-Tiny as the encoder and Attention U-Net as the decoder. It uses SE-based cross-attention fusion to segment colorectal polyps from endoscopic images. In tests on the Kvasir-SEG dataset, PolypSegNet consistently outperformed standard models like U-Net and Attention U-Net across various evaluation metrics. Specifically, PolypSegNet yielded a Dice-Score of 0.942, an IoU of 0.9038 and a Precision of 0.9471. This shows its excellent ability to segment, particularly for small and irregularly shaped polyps. Ablation studies confirmed the important role of each architectural component, including ConvNeXt, SE-fusion, and Attention Gates, in the overall performance of the model. 5.2 Limitations Despite its strong performance, the study has some limitations: Dataset Diversity : The model was evaluated only for the Kvasir-SEG [ 19 ] dataset where it performed well. But there is still no proof of generalization across various datasets like CVC-ClinicDB, ETIS-Larib. Small Polyp Bias : Although attention gates improve fine segmentation, very tiny or blurred polyps still pose a challenge in certain cases. Computation Cost : Integration of ConvNeXt and attention mechanisms increases the number of parameters (~ 38.8M), making deployment on real-time embedded systems more resource-demanding [ 24 ]. 5.3 Future Work Several avenues can extend this research in meaningful directions: Dataset diversity : The model was tested on the Kvasir-SEG dataset only. While it works well, the generalization on different datasets (e.g., CVC-ClinicDB, ETIS-Larib) is not validated yet [ 25 ] [ 26 ]. Lightweight Version : Future work will investigate light-weight versions, such as MobileViT or Tiny Attention U-Net, in order to achieve real-time processing within colonoscopy systems [ 27 ]. Semi-Supervised Learning : As biomedical data is often scarce and difficult to annotate, integrating semi-supervised or self-supervised learning paradigms may enhance robustness [ 28 ]. 3D Volumetric Extension : Extending the framework to 3D medical imaging such as for CT colonography may provide even higher context for polyp boundary learning [ 29 ]. Explainable AI (XAI) Incorporating explainability tools like Grad-CAM, Attention Rollout, or SHAP will allow clinicians to better trust and interpret the predictions [ 30 ]. Declarations 5.4 Data Availability and Statement The dataset analyzed in this research, Kvasir-SEG for colorectal polyp segmentation, is freely accessible at The dataset analyzed in this research, Kvasir-SEG for colorectal polyp segmentation, is freely accessible at https://www.kaggle.com/datasets/debeshjha1/kvasirseg. 5.5 Conflict Of Interest The Author Declares no conflict of interest in this research. 5.6 Funding The research receives no specific grants from any institutions, public or commercial industries. 5.7 Acknowledgement The authors would like to be grateful towards the immense contribution of Md Khaled Sohel, who is the Assistant Professor of Software Engineering department of Daffodil International University. Md Khaled Sohel solely guided the team for completion of this Artificial Intelligence based Biomedical Imaging Research. References K. H. J. A. U. Z. O. J. J. A. L. Y. K. K. M. A. M. L. G. K. H. M. 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Y. L. C. Q. S. M. Z. L. Kai Han, "Deep semi-supervised learning for medical image segmentation: A review," Expert Systems with Applications, vol. 245, p. 123052, 2024. Z. J. D. S. W. K. A.-R. A. F. Y. Lin H, "Volumetric medical image segmentation via fully 3D adaptation of Segment Anything Model," Biocybernetics and Biomedical Engineering, vol. 45, no. 1, pp. 1-10, 2025. S. Y. N. M. S. C. K. N. F. C. Borys K, "Explainable AI in medical imaging: An overview for clinical practitioners – Saliency-based XAI approaches," European Journal of Radiology, vol. 162, p. 110787, 2023. 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. 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Curve\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7102819/v1/04e53c1956a34c22f1f3ab19.png"},{"id":86769536,"identity":"e6194e1c-5d0a-4a4b-babf-dabba97bb60f","added_by":"auto","created_at":"2025-07-15 11:34:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":347747,"visible":true,"origin":"","legend":"\u003cp\u003eQualitative Comparison of PolypSegNet with U-Net and Attention U-Net\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7102819/v1/43e18aa3b9509725a89745c9.png"},{"id":86769534,"identity":"3a96f73d-286f-4b9b-bf16-8aa8246b1ef9","added_by":"auto","created_at":"2025-07-15 11:34:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":166182,"visible":true,"origin":"","legend":"\u003cp\u003eThe performance of PolypSegNet\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7102819/v1/6a70e5f60ebc9e938a8e6948.png"},{"id":86772239,"identity":"7a27f94e-3974-4450-8dce-323a241b89da","added_by":"auto","created_at":"2025-07-15 11:58:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2149071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7102819/v1/799f88d3-cf4d-423f-b347-f61a7c7f1655.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePolypSegNet: A Hybrid ConvNeXt-Tiny and Attention U-Net Framework for Accurate Colorectal Polyp Segmentation\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is one of the most common and life-threatening cancers in the global context. The prompt identification of polyps during colonoscopy is very important for stopping it\u003cstrong\u003e\u0026nbsp;[1]\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;[2]\u003c/strong\u003e. Automated segmentation of colorectal polyps in endoscopic images helps doctors by giving them quick and accurate outlines [3]\u003cstrong\u003e.\u003c/strong\u003e But this job is hard because polyps have strange shapes, don\u0026apos;t stand out against adjacent tissue, and have artifacts\u0026nbsp;\u003cstrong\u003e[4]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTraditional convolutional neural networks (CNNs), especially the U-Net architecture, have been very important for medical segmentation\u003cstrong\u003e\u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-begin'\u003e\u003c/span\u003e\u003cspan lang=EN-US style='mso-ansi-language: EN-US'\u003eCITATION Ron15 \\l 1033 \u003c/span\u003e\u003cspan style='mso-element:field-separator'\u003e\u003c/span\u003e\u003c![endif]--\u003e [5]\u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-end'\u003e\u003c/span\u003e\u003c![endif]--\u003e\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe encoder-decoder structure of U-Net, which includes skip connections, keeps the spatial context and has worked well on colonoscopy data. Still, these kinds of models have a hard time capturing both the big picture and minor details at the same time, especially in low-light scenes or with small polyps\u0026nbsp;\u003cstrong\u003e[6]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eRecent progress combines CNN backbones with attention or positional embedding modules to fill in semantic gaps. For example, ConvNeXt-based models greatly improve the accuracy of segmentation by using better hierarchical representation\u003cstrong\u003e\u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-begin'\u003e\u003c/span\u003e\u003cspan lang=EN-US style='mso-ansi-language: EN-US'\u003eCITATION Luh22 \\l 1033 \u003c/span\u003e\u003cspan style='mso-element:field-separator'\u003e\u003c/span\u003e\u003c![endif]--\u003e [7]\u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-end'\u003e\u003c/span\u003e\u003c![endif]--\u003e\u003c/strong\u003e. Hybrid networks that combine U-Net with transformers or attention gates also improve the segmentation of unclear areas\u003cstrong\u003e\u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-begin'\u003e\u003c/span\u003e\u003cspan lang=EN-US style='mso-ansi-language: EN-US'\u003eCITATION Pan25 \\l 1033 \u003c/span\u003e\u003cspan style='mso-element:field-separator'\u003e\u003c/span\u003e\u003c![endif]--\u003e [8]\u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-end'\u003e\u003c/span\u003e\u003c![endif]--\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe suggest PolypSegNet, a hybrid framework that combines the global feature extraction of the ConvNeXt-Tiny encoder with the channel-spatial attention blocks of the Attention U-Net decoder. This design works well to segregate colorectal polyps into different textures and boundaries, making it perfect for use in a variety of imaging situations.\u003c/p\u003e\n\u003cp\u003eWe test our model on the Kvasir-SEG dataset and get a remarkable dice score of 0.9420, an IoU of 0.8930, and an F1-score of 0.9415, which outperforms both standard U-Net and other recent models. The results show that using modern CNN architectures with attention modules makes the system more accurate and stable, which is good for clinical use.\u003c/p\u003e"},{"header":"2. Related Work","content":"\u003cp\u003eAutomated polyp segmentation in colonoscopy images has attained a lot of attention because it is very important for diagnosis of colorectal cancer at an early stage. In the era of Artificial Intelligence, medical image segmentation has made efficient use of traditional deep learning architectures, especially encoder-decoder frameworks like U-Net\u0026nbsp;[9].The first U-Net did very well on biomedical images by using skip connections to get spatial information back during upsampling. Chen Si surveyed on his paper about the limitations and scopes of U-Net based architecture for colorectal polyp segmentation\u0026nbsp;[10], there was shown that U-Net has trouble modeling long-range dependencies. To fix this, attention mechanisms were introduced\u0026nbsp;[11]. Attention U-Net enhances U-Net to focus on important parts of an image, which makes it better at segmenting small, complicated lesions like polyps. In the same way, U-Net++ used nested and dense skip pathways to make multi-scale feature fusion better\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eRecent improvements use transformer-based modules to make global context modeling better. TransUNet\u0026nbsp;[13] uses a Vision Transformer (ViT) decoder and CNN encoders to combine them. It uses the global self-attention mechanism to pick up on all the features at once\u0026nbsp;[14]. These hybrid models are very powerful, but they are also very expensive to run and often need big datasets to train well [15]\u003cstrong\u003e.\u003c/strong\u003eAt the same time, ConvNeXt\u0026nbsp;[16], a new convolutional architecture, showed that CNNs can perform as well as transformers on vision tasks if their architecture is carefully tuned. It uses large kernel convolutions and layer normalization to stay efficient and simple, which makes it a great encoder for medical uses. Polyp-PVT\u0026nbsp;[17], for instance, utilizes Pyramid Vision Transformers with multi-scale fusion but isn't lightweight in realization for real-time clinical use. PraNet [18] suggests an inverse attention mechanism but is more suitably used for binary segmentation and doesn't leverage modern CNNs like ConvNeXt.\u003c/p\u003e\n\u003cp\u003eTo overcome these limitations, our research proposes the \u003cstrong\u003ePolypSegNet\u003c/strong\u003e architecture as a combination of the strengths of ConvNeXt-Tiny (hierarchical representation richness) and Attention U-Net (spatial accuracy). Its dual architecture ensures both global context and accurate boundary delineation at low computational complexity.\u003c/p\u003e"},{"header":"3.\tMethodology","content":"\u003cp\u003eHere, we present a comprehensive description of the hybrid architecture of our proposed model, PolypSegNet, for the precise segmentation of colorectal polyps. The process is divided into five main sections: preprocessing of the dataset, architectural framework, fusion methods, training process, and evaluation metrics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 \u0026nbsp;Dataset and Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kvasir-SEG dataset [19] \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eis an open-access colorectal cancer imaging collection that includes 1000 polyp images along with their corresponding pixel wise segmentation masks. Each image with masks was resized to 256\u0026times;256 resolution and normalized to the [0,1][0, 1][0,1] scale. The data was divided into training, validation, and testing sets using a standard 70:20:10 ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreprocessing Steps\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eResizing all images and masks to 256\u0026times;256.\u003c/li\u003e\n \u003cli\u003eNormalizing pixel intensities.\u003c/li\u003e\n \u003cli\u003eData augmentation: random flipping, rotation, and zooming to improve generalization.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Proposed Architecture:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePolypSegNet\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the Figure-1, we have shown the system flow of the PolypSegNet. The proposed PolypSegNet framework features a hybrid encoder-decoder architecture that combines ConvNeXt-Tiny as the encoder and Attention U-Net as the decoder. A Squeeze-and-Excitation (SE) fusion block is also included between the encoder and decoder to improve spatial channel-wise feature recalibration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEncoder: ConvNeXt-Tiny \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConvNeXt-Tiny\u003c/strong\u003e is a modern ResNet-like backbone that uses hierarchical convolutional blocks. It acts as a strong feature extractor and generates multi-level representations:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStage 1: 64\u0026times;64\u0026times;96\u003cbr\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStage 2: 32\u0026times;32\u0026times;192\u003cbr\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStage 3: 16\u0026times;16\u0026times;384\u003cbr\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStage 4 (Bottleneck): 8\u0026times;8\u0026times;768\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese feature maps are fused through the decoder after being sent via skip connections. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSE Fusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo improve discriminative capabilities, SE blocks are used to recalibrate the encoder outputs by modeling channel dependencies. This fusion boosts polyp-relevant features before up sampling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecoder: Attention U-Net\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe decoder relies on Attention U-Net blocks, which use attention gating to focus on important encoder features during skip connections. The hierarchical decoding procedure includes:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStage 1: 8\u0026times;8\u0026times;768 \u0026rarr; 16\u0026times;16\u0026times;512\u003c/li\u003e\n \u003cli\u003eStage 2: 16\u0026times;16\u0026times;512 \u0026rarr; 32\u0026times;32\u0026times;256\u003c/li\u003e\n \u003cli\u003eStage 3: 32\u0026times;32\u0026times;256 \u0026rarr; 64\u0026times;64\u0026times;128\u003c/li\u003e\n \u003cli\u003eStage 4: 64\u0026times;64\u0026times;128 \u0026rarr; 128\u0026times;128\u0026times;64\u003c/li\u003e\n \u003cli\u003eStage 5: 128\u0026times;128\u0026times;64 \u0026rarr; 256\u0026times;256\u0026times;32\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA final \u003cstrong\u003e1\u0026times;1 convolution + sigmoid\u003c/strong\u003e produces the binary segmentation mask.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Training Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eLoss Function\u003c/strong\u003e: Combination of \u003cstrong\u003eBinary Cross-Entropy (BCE)\u003c/strong\u003e and \u003cstrong\u003eDice Loss\u003c/strong\u003e was used to optimize boundary precision and region overlap. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eOptimizer\u003c/strong\u003e: For optimization, we used Adam, where the initial learning rate was 0.001. And then gradually we adjusted it via a learning rate scheduler.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eEpochs\u003c/strong\u003e: Trained for 25 epochs on Google Colab using an NVIDIA T4 GPU.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eBatch Size\u003c/strong\u003e: 16\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eCallbacks\u003c/strong\u003e: EarlyStopping and Model Checkpoint were used to save the best-performing model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Evaluation Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate segmentation quality, the following metrics were computed for the hybrid framework:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Model Visualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo offer a clear understanding of the internal workings of the proposed \u003cstrong\u003e\u003cem\u003ePolypSegNet\u003c/em\u003e\u003c/strong\u003e architecture, a schematic visualization is presented in \u003cstrong\u003eFigure 2\u003c/strong\u003e. This illustration captures the complete end-to-end flow of information from input image to binary segmented output.\u003c/p\u003e\n\u003cp\u003eAt the \u003cstrong\u003eleft side\u003c/strong\u003e, the input endoscopic image is first passed through the \u003cstrong\u003eConvNeXt-Tiny encoder\u003c/strong\u003e, which hierarchically extracts spatial features at four progressive levels:\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp; \u0026nbsp;\u0026nbsp;Level 1: 64\u0026times;64\u0026times;96\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp; \u0026nbsp;\u0026nbsp;Level 2: 32\u0026times;32\u0026times;192\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp; \u0026nbsp;\u0026nbsp;Level 3: 16\u0026times;16\u0026times;384\u003c/p\u003e\n\u003cp\u003e●\u0026nbsp; \u0026nbsp;\u0026nbsp;Level 4: 8\u0026times;8\u0026times;768 (bottleneck)\u003c/p\u003e\n\u003cp\u003eThese features are efficiently encoded through convolutional stages that include residual and normalization layers [20].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The\u003cstrong\u003e\u0026nbsp;Squeeze-and-Excitation (SE)\u003c/strong\u003e fusion block is crucial at this stage, as it learns to amend the feature channels by deciding which one is to emphasize or suppress [21]. This improves the encoder\u0026rsquo;s ability to focus on polyp-related features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, on the right side, the decoder begins a series of upsampling operations. Each decoding stage uses Attention U-Net blocks, which apply soft attention to selecting only the most relevant encoder features during skip connections [22] [23]. This method reduces background noise and improves segmentation accuracy for small or unclear polyps.\u003c/p\u003e\n\u003cp\u003e● The decoder first expands the bottleneck (8\u0026times;8\u0026times;768) back to full resolution through five stages.\u003c/p\u003e\n\u003cp\u003e● Attention gates at each level improve the combination of encoder-decoder features.\u003c/p\u003e\n\u003cp\u003e● A\u0026nbsp;\u003cstrong\u003e1\u0026times;1 convolution laye\u003c/strong\u003er followed by a sigmoid activation creates the final binary segmentation mask.\u003c/p\u003e\n\u003cp\u003eAt the far right of the diagram, the output shows a binary mask that highlights the polyp regions segmented from the input image.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis visual architecture can provide a complete overview of \u0026nbsp; the PolypSegNet pipeline. It showcased its hybrid fusion architecture, multi-scale feature handling and attention-enhanced skip connections. Together these elements help improve segmentation accuracy. \u0026nbsp;\u003c/p\u003e"},{"header":"4. Experimental Results and Discussions","content":"\u003cp\u003eThis section provides a comprehensive evaluation of the proposed \u003cb\u003ePolypSegNet framework\u003c/b\u003e. We present both quantitative and qualitative comparisons with existing baseline models (U-Net and Attention U-Net) to demonstrate the novelty of the hybrid architecture grounded in ConvNeXt-Tiny and Attention U-Net. Additionally training behavior and ablation insights are discussed to validate individual components of the framework.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Quantitative Evaluation\u003c/h2\u003e\u003cp\u003eWe assess the segmentation performance with five standard metrics. These metrics are the Dice Coefficient, Intersection over Union (IoU), Precision, Recall, and F1 Score. We calculate all results using the validation set from the Kvasir-SEG dataset.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of PolypSegNet with baseline models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDice Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIOU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF1 Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eU-Net (Baseline)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.7541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.7550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAttention U-Net\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolypSegNet\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.9420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.9456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in table-1, PolypSegNet clearly performs better than both U-Net and Attention U-Net for all evaluation metrics. The model achieves a \u003cb\u003eDice Coefficient of 0.942 and an IoU of 0.893\u003c/b\u003e, indicating a strong overlap between the predicted and ground-truth masks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Training and Validation Behavior\u003c/h2\u003e\u003cp\u003eThe training was conducted for 25 epochs using the Adam optimizer, where the scheduler was ReduceLROnPlateau. The training and validation performance metrics were recorded per epoch, showing smooth convergence and generalization.\u003c/p\u003e\u003cp\u003eThe model achieved its \u003cb\u003ebest performance at Epoch 18\u003c/b\u003e, with:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTraining Accuracy\u003c/b\u003e: 98.00%\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTraining Loss\u003c/b\u003e: 0.0276\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eValidation Accuracy\u003c/b\u003e: 96.26%\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eValidation Loss\u003c/b\u003e: 0.0784\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Qualitative Results\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe performed qualitative comparisons for further validation. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the visual results from sample validation images. The comparison was held by original image with Ground Truth Mask vs Predicted Mask for -\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eU-Net\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAttention U-Net\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePolypSegNet\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, PolypSegNet consistently yields sharper, more complete segmentations, especially for small and irregularly shaped polyps, where baseline models struggle.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Ablation Insights\u003c/h2\u003e\u003cp\u003eTo evaluate the individual impact of each main component in the PolypSegNet architecture. So, we carried out an ablation study. We removed one module at a time - leaving the rest of the architecture unchanged. This method shows how much each module contributes to the model's overall performance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAblation insights of PolypSegNet\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFull PolypSegNet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBest overall performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConvNeXt Encoder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;3.5% Dice drop; loss of rich features\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttention Gates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReduced ability to localize fine boundaries\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSE Fusion Module\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;2% Precision drop; unstable convergence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFrom Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, each of the aborted components resulted in a clear degradation in segmentation's quality, measured in Dice Score, Precision, and Recall.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eKey Takeaways\u003c/b\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eConvNeXt Encoder\u003c/b\u003e plays a critical role in extracting deep semantic features that enhance segmentation accuracy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAttention Gates\u003c/b\u003e help localize polyps more precisely, especially in ambiguous or fine-boundary regions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSE Fusion\u003c/b\u003e facilitates better channel-wise feature recalibration, improving both learning stability and predictive precision.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese findings show that the proposed improvements are not unnecessary. They work together to boost performance gradually. The ablation results support the idea that PolypSegNet\u0026rsquo;s design is both useful and well-founded.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion and Future Work","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Summary of Findings\u003c/h2\u003e\n \u003cp\u003eThis study presents \u003cstrong\u003ePolypSegNet\u003c/strong\u003e, a new hybrid deep learning framework that combines ConvNeXt-Tiny as the encoder and Attention U-Net as the decoder. It uses SE-based cross-attention fusion to segment colorectal polyps from endoscopic images. In tests on the Kvasir-SEG dataset, PolypSegNet consistently outperformed standard models like U-Net and Attention U-Net across various evaluation metrics.\u003c/p\u003e\n \u003cp\u003eSpecifically, PolypSegNet yielded a Dice-Score of 0.942, an IoU of 0.9038 and a Precision of 0.9471. This shows its excellent ability to segment, particularly for small and irregularly shaped polyps. Ablation studies confirmed the important role of each architectural component, including ConvNeXt, SE-fusion, and Attention Gates, in the overall performance of the model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Limitations\u003c/h2\u003e\n \u003cp\u003eDespite its strong performance, the study has some limitations:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eDataset Diversity\u003c/strong\u003e: The model was evaluated only for the Kvasir-SEG [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] dataset where it performed well. But there is still no proof of generalization across various datasets like CVC-ClinicDB, ETIS-Larib.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eSmall Polyp Bias\u003c/strong\u003e: Although attention gates improve fine segmentation, very tiny or blurred polyps still pose a challenge in certain cases.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eComputation Cost\u003c/strong\u003e: Integration of ConvNeXt and attention mechanisms increases the number of parameters (~\u0026thinsp;38.8M), making deployment on real-time embedded systems more resource-demanding [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Future Work\u003c/h2\u003e\n \u003cp\u003eSeveral avenues can extend this research in meaningful directions:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eDataset diversity\u003c/strong\u003e: The model was tested on the Kvasir-SEG dataset only. While it works well, the generalization on different datasets (e.g., CVC-ClinicDB, ETIS-Larib) is not validated yet [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eLightweight Version\u003c/strong\u003e: Future work will investigate light-weight versions, such as MobileViT or Tiny Attention U-Net, in order to achieve real-time processing within colonoscopy systems [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eSemi-Supervised Learning\u003c/strong\u003e: As biomedical data is often scarce and difficult to annotate, integrating \u003cstrong\u003esemi-supervised\u003c/strong\u003e or \u003cstrong\u003eself-supervised\u003c/strong\u003e learning paradigms may enhance robustness [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e3D Volumetric Extension\u003c/strong\u003e: Extending the framework to 3D medical imaging such as for CT colonography may provide even higher context for polyp boundary learning [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eExplainable AI (XAI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIncorporating explainability tools like Grad-CAM, Attention Rollout, or SHAP will allow clinicians to better trust and interpret the predictions [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5.4 Data Availability and Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed in this research, Kvasir-SEG for colorectal polyp segmentation, is freely accessible at The dataset analyzed in this research, Kvasir-SEG for colorectal polyp segmentation, is freely accessible at https://www.kaggle.com/datasets/debeshjha1/kvasirseg.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Conflict Of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Author Declares no conflict of interest in this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.6 Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research receives no specific grants from any institutions, public or commercial industries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.7 Acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to be grateful towards the immense contribution of Md Khaled Sohel, who is the Assistant Professor of Software Engineering department of Daffodil International University. Md Khaled Sohel solely guided the team for completion of this Artificial Intelligence based Biomedical Imaging Research.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eK. H. J. A. U. Z. O. J. J. A. L. Y. K. K. M. A. M. L. G. K. H. M. Hossain MS, \u0026quot; Colorectal Cancer: A Review of Carcinogenesis, Global Epidemiology, Current Challenges, Risk Factors, Preventive and Treatment Strategies. Cancers (Basel),\u0026quot; vol. 14(7), p. 1732, 2022 .\u003c/li\u003e\n \u003cli\u003eC. G. E. J. Cianci N, \u0026quot;Colorectal cancer: prevention and early diagnosis,\u0026quot; vol. 52, no. 5, pp. 251-257, 2024.\u003c/li\u003e\n \u003cli\u003eY.-C. L. C.-C. C. M.-C. C. T.-Y. C. Y.-L. H. S.-W. C. C.-L. L. Y.-J. C. C.-Y. L. 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Borys K, \u0026quot;Explainable AI in medical imaging: An overview for clinical practitioners \u0026ndash; Saliency-based XAI approaches,\u0026quot; European Journal of Radiology, vol. 162, p. 110787, 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Daffodil International University","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":"Colorectal Polyp Segmentation, Biomedical Image Analysis, Hybrid Deep Learning Architecture, ConvNeXt-Tiny, Attention U-Net, Kvasir-SEG Dataset","lastPublishedDoi":"10.21203/rs.3.rs-7102819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7102819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer is still one of the most common causes of cancer deaths around the world. Finding polyps early is very important for preventing this type of cancer. However, it is very hard to automatically segment colorectal polyps in endoscopic images because they are not always the same shape, have low contrast, and have different levels of light. We present PolypSegNet in this paper. It is a new hybrid deep learning framework that combines the feature extraction abilities of ConvNeXt-Tiny with the spatial awareness and localization abilities of Attention U-Net. Our architecture combines a lightweight ConvNeXt encoder with attention-boosted skip connections and decoder blocks. This makes it possible to keep context and draw precise boundaries. We test our model on the Kvasir-SEG dataset, which is available to the public, and show that PolypSegNet does a better job of segmenting than traditional U-Net and other baseline models. Specifically, our method has a dice coefficient of 0.9420, an IoU of 0.8930, and an F1-score of 0.9415 which shows how well it works. The results show that combining hierarchical ConvNeXt features with attention mechanisms makes polyp detection much more accurate. This work points to a promising way to segment colorectal polyps in real time and with high accuracy in clinical practice.\u003c/p\u003e","manuscriptTitle":"PolypSegNet: A Hybrid ConvNeXt-Tiny and Attention U-Net Framework for Accurate Colorectal Polyp Segmentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 11:33:58","doi":"10.21203/rs.3.rs-7102819/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":"c9a50361-67af-4e3f-a8d0-d4e2d0b1d267","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51409244,"name":"Bioinformatics"},{"id":51409245,"name":"Biochemical Research Methods"},{"id":51409246,"name":"Computational Biology"}],"tags":[],"updatedAt":"2025-07-15T11:33:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 11:33:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7102819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7102819","identity":"rs-7102819","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}