A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms | 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 A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms preeti katiyar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3749398/v4 This work is licensed under a CC BY 4.0 License Status: Posted Version 4 posted You are reading this latest preprint version Show more versions Abstract Breast cancer poses a significant global health threat to women, underscoring the crucial need for reliable and effective screening approaches. The utilization of computer-aided diagnostic (CAD) systems, leveraging mammograms, enables early detection, diagnosis, and treatment of breast cancer, thereby offering vital support in combating this disease. This study introduces a unique deep-learning model that uses transfer learning to identify and categorize breast cancer automatically. Several recent studies have shown that deep convolutional neural networks (DCNNs) can be used to diagnose breast cancer in mammograms with performances comparable to or even superior to those of human experts. The proposed model extracts features from the Mammographic Image Analysis Society (MIAS) dataset using pre-trained convolutional neural network (CNN) architectures such as ResNet50 and VGG-16. This revolutionary deep-learning model has the potential to improve the efficiency and accuracy of breast cancer detection and categorization. Biomedical Engineering Breast cancer Screening techniques Computer-aided diagnostic (CAD) systems Mammograms Transfer learning Deep learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 4 posted You are reading this latest preprint version Show more versions 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-3749398","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274374500,"identity":"6767ff9b-0a47-4069-ba00-c60917df6f3d","order_by":0,"name":"preeti katiyar","email":"data:image/png;base64,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","orcid":"","institution":"GGSIPU","correspondingAuthor":true,"prefix":"","firstName":"preeti","middleName":"","lastName":"katiyar","suffix":""}],"badges":[],"createdAt":"2023-12-13 16:27:14","currentVersionCode":4,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3749398/v4","doiUrl":"https://doi.org/10.21203/rs.3.rs-3749398/v4","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53748061,"identity":"723afd46-9c02-436b-9dc9-a1b343e72a19","added_by":"auto","created_at":"2024-03-29 18:20:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":427803,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3749398/v4_covered_2f14d79f-b458-4533-93a3-780d58f7e145.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"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":"Breast cancer, Screening techniques, Computer-aided diagnostic (CAD) systems, Mammograms, Transfer learning, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-3749398/v4","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3749398/v4","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer poses a significant global health threat to women, underscoring the crucial need for reliable and effective screening approaches. 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