Classification and Interpretation of Histopathology Images: Leveraging Ensemble of EfficientNetV1 and EfficientNetV2 Models

Classification and Interpretation of Histopathology Images: Leveraging Ensemble of EfficientNetV1 and EfficientNetV2 Models | 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 Classification and Interpretation of Histopathology Images: Leveraging Ensemble of EfficientNetV1 and EfficientNetV2 Models Mahdi Azmoodeh Kalati, Hasti Shabani, Mohammad Sadegh Maghareh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6040188/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 Breast cancer is the second leading cause of cancer-related deaths among women, following lung cancer, as of 2024. Conventional cancer diagnosis relies on manual examination of biopsied tissues by pathologists which is a time-consuming process and based on pathologist experience may vary. Early detection and accurate diagnosis are critical for effective treatment planning and patient care. The invention of whole-slide scanners has revolutionized this process by enabling the adoption of Computer-Aided Detection (CAD) systems for automated analysis. Convolutional Neural Networks (CNNs) within CAD systems play a pivotal role in the automated classification of breast tissues. This study utilizes state-of-the-art CNNs called EfficientNetV1 and EfficientNetV2 for binary classification of BreakHis dataset ,a collection of histopathological images categorized as benign and malignant breast tissues. To address the challenge posed by the limited availability of annotated data, data augmentation and transfer learning techniques were applied. Model interpretability was enhanced using the Grad-CAM technique, which generates localization maps highlighting critical regions relevant to predictions. Finally, ensemble learning is employed for further improving performance. we utilized unweighted averaging and majority voting to combine predictions of multiple trained models. Furthermore, We define two ensemble architecture combining different trained EfficientNets. The proposed framework was able to achieve a classification accuracy of 99.58% outperforming conventional CNN models on BreakHis dataset. This study underscores the potential of ensemble learning to improve diagnostics accuracy in breast cancer detection. Artificial Intelligence and Machine Learning Nuclear Medicine & Medical Imaging Biomedical Engineering Medical Informatics Cancer Biology Breast Cancer Convolutional Neural Networks (CNN) Transfer Learning Grad-CAM Ensemble Methods Majority Voting Unweighted averaging Full Text 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-6040188","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416388843,"identity":"a8305859-a70e-4522-a9fa-d50d547be41d","order_by":0,"name":"Mahdi Azmoodeh Kalati","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"Azmoodeh","lastName":"Kalati","suffix":""},{"id":416390973,"identity":"b1723209-d831-4610-b259-73bc4012ab40","order_by":1,"name":"Hasti Shabani","email":"","orcid":"https://orcid.org/0000-0002-1573-1485","institution":"Shahid 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