Early Breast Cancer Detection Among Women Using Deep Learning Algorithms: A Case Study of Oyo State, Nigeria

Early Breast Cancer Detection Among Women Using Deep Learning Algorithms: A Case Study of Oyo State, Nigeria | 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 Early Breast Cancer Detection Among Women Using Deep Learning Algorithms: A Case Study of Oyo State, Nigeria Olatunji Oluwatoyin Olukunle, Tayo Peter Ogundunmade, Afolabi Oluranti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9401858/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract Breast cancer has remained one of the leading causes of death in Nigerian women, mostly due to late diagnosis and inadequate diagnostic resources. We investigate the feasibility of using deep learning for enhancing detection in Oyo State, Nigeria, through the application of CNNs as compared to the human diagnostic process. Hospital and diagnostic center imaging was used and analyzed with Residual Network-50, VGG16, and Efficient Network deep learning models. Human diagnosticians reported 75% accuracy, but the Residual Network-50 model showed superior detection results of 94% accuracy, 93% sensitivity, and 95% specificity. Efficient Network was equally competitive, showing a balanced accuracy of 92%, but required less computing power. The deep learning models, especially the Residual Network-50 and Efficient Network models, showed potential to enhance the accuracy in breast cancer detection, especially in resource-limited areas. Overcoming limitations of implementation will assist in bringing down the mortality rates and increase the well-being of women in Oyo State and in Nigeria in general. Breast Cancer Deep Learning Residual Network-50 Efficient Network Convolutional Neural Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 04 May, 2026 Editor invited by journal 30 Apr, 2026 Submission checks completed at journal 28 Apr, 2026 First submitted to journal 28 Apr, 2026 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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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-9401858","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636413788,"identity":"2f23dc7b-8cc3-407b-839c-78229f9f8c61","order_by":0,"name":"Olatunji Oluwatoyin Olukunle","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Olatunji","middleName":"Oluwatoyin","lastName":"Olukunle","suffix":""},{"id":636413789,"identity":"f4587620-cdd0-4b1e-9fea-cb6b126a2f8b","order_by":1,"name":"Tayo Peter Ogundunmade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYLACHjCZACblYFyitDA2AElj0rUkNhDSws+/xuzB27Y70fztyccf/GyzSd9w/OzBBx8Y7OR0G7BrkZzxxtxwbtuz3BlnniU29ral5W44k5dsOIMh2djsAHYtBjfOmEnzth3ObbiRY9gAYmw4kGMmzcNwIHEbIS3zb+R/bPzbdjjd4PwbAlrO90C0bLiRw9gMZCQY3CBgi+QMtjLJOecO524888xwtsy5NMOZN94YG84wwO0Xfv7D2yTelB3OnXc8+cHHN2U28nzncwwffKiwk8OlhUEiAYnDyMbAoABWaYBDOdgaFLP+MDDIN+BRPQpGwSgYBSMSAAChrmkw0MMEpQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Tayo","middleName":"Peter","lastName":"Ogundunmade","suffix":""},{"id":636413790,"identity":"3465e3ba-69e3-4da3-ae5c-b288ffa5f792","order_by":2,"name":"Afolabi Oluranti","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Afolabi","middleName":"","lastName":"Oluranti","suffix":""}],"badges":[],"createdAt":"2026-04-13 09:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9401858/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9401858/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109076276,"identity":"b6bd5a02-7fa1-4b03-9e90-d0091e86aaa6","added_by":"auto","created_at":"2026-05-12 11:01:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":279540,"visible":true,"origin":"","legend":"","description":"","filename":"polypaper22026new1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9401858/v1_covered_bce932db-7bb0-4929-88c7-f127a5b54f60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Breast Cancer Detection Among Women Using Deep Learning Algorithms: A Case Study of Oyo State, Nigeria","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Artificial Intelligence](https://bmcartificialintel.biomedcentral.com)","snPcode":"44398","submissionUrl":"https://submission.nature.com/new-submission/44398/3","title":"BMC Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Deep Learning, Residual Network-50, Efficient Network, Convolutional Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-9401858/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9401858/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Breast cancer has remained one of the leading causes of death in Nigerian women, mostly due to late diagnosis and inadequate diagnostic resources. 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