A comparative ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer detection

A comparative ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer detection | 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 Article A comparative ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer detection Madhumita Pal, Ganapati Panda, Ranjan Mohapatra, Adyasha Rath, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4620451/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 (BC) is the most frequently occurring cancer in women after lung cancer. There are different stages of breast cancer. Among them, Invasive ductal BC causes the maximum number of deaths in women. Different radio imaging techniques are available to diagnose this fatal disease. With the development of different radiographic imaging techniques, it is now possible to identify this fatal condition early on. However, qualified radiologists and pathologists must analyze the radiographic and Histopathological images. The procedure is expensive and prone to mistakes, as radiologists and pathologists are human beings. In this paper, three deep learning models such as Vision Transformer (ViT), Convmixer and Visual Geometry Group-19 (VGG-19), are proposed for the detection and classification of different breast cancer tumours using Breast cancer histopathological (Break His) image database. The performance of each of these models is evaluated using an 80:20 training scheme and measured in terms of accuracy, precision, recall, loss, F1 score and area under the curve. From the simulation result, we found that ViT performs best for binary classification of breast cancer tumours with accuracy, precision, recall and F1-score of 99.89%,98.29%,98.29% and 98.29%, respectively. Also, ViT gives the best performance in terms of accuracy, Precision, recall and F1-score 98.21%, 89.84% and 89.97%, respectively, for eight class classifications of breast histopathological images. Then, we have an ensemble ViT-Convmixer model for detecting breast cancer and observe that the ensemble model's performance degrades compared to the ViT model with an accuracy of 95 and 85 percent. We have also compared the performance of the proposed best model with the other existing models. The proposed model can also detect other diseases with improved accuracy. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Breast cancer Deep learning Vision transformer Convmixer VGG-19 Full Text Additional Declarations No competing interests reported. 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-4620451","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326944144,"identity":"9ecd239f-c2c4-49b5-8f50-ad35f78d6ec7","order_by":0,"name":"Madhumita Pal","email":"","orcid":"","institution":"Government College of Engineering, Keonjhar, Odisha 758002, India","correspondingAuthor":false,"prefix":"","firstName":"Madhumita","middleName":"","lastName":"Pal","suffix":""},{"id":326944145,"identity":"c00f7408-e113-4b1a-ba99-df8315c719ca","order_by":1,"name":"Ganapati Panda","email":"","orcid":"","institution":"C. V. 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There are different stages of breast cancer. Among them, Invasive ductal BC causes the maximum number of deaths in women. Different radio imaging techniques are available to diagnose this fatal disease. With the development of different radiographic imaging techniques, it is now possible to identify this fatal condition early on. However, qualified radiologists and pathologists must analyze the radiographic and Histopathological images. The procedure is expensive and prone to mistakes, as radiologists and pathologists are human beings. In this paper, three deep learning models such as Vision Transformer (ViT), Convmixer and Visual Geometry Group-19 (VGG-19), are proposed for the detection and classification of different breast cancer tumours using Breast cancer histopathological (Break His) image database. The performance of each of these models is evaluated using an 80:20 training scheme and measured in terms of accuracy, precision, recall, loss, F1 score and area under the curve. From the simulation result, we found that ViT performs best for binary classification of breast cancer tumours with accuracy, precision, recall and F1-score of 99.89%,98.29%,98.29% and 98.29%, respectively. Also, ViT gives the best performance in terms of accuracy, Precision, recall and F1-score 98.21%, 89.84% and 89.97%, respectively, for eight class classifications of breast histopathological images. Then, we have an ensemble ViT-Convmixer model for detecting breast cancer and observe that the ensemble model's performance degrades compared to the ViT model with an accuracy of 95 and 85 percent. We have also compared the performance of the proposed best model with the other existing models. 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