Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy

Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy | 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 Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy Naim Ajlouni, Adem Özyavaş, Firas Ajlouni, Mustafa Takaoğlu, Faruk Takaoğlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4794714/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 remains a leading cause of mortality among women globally, underscoring the urgent need for improved diagnostic and staging techniques to enhance patient outcomes. This study aims to automate the TNM staging of breast cancer using a hybrid approach that integrates Convolutional Neural Networks (CNNs), edge detection methods, and Self-Organizing Maps (SOMs). Utilizing the Duke Breast Cancer MRI dataset, which provides detailed MRI scans crucial for accurate tumor characterization, the research addresses the limitations of traditional TNM staging, which often relies on manual interpretation by radiologists and can lead to inconsistencies and inaccuracies. Our approach combines CNNs with advanced edge detection algorithms and SOMs to automate and enhance the accuracy of breast cancer staging. The hybrid model effectively identifies and delineates tumor boundaries and critical anatomical features, offering a more reliable and objective evaluation. Notably, this method improves accuracy from 93% with conventional CNN models to 98%, representing a significant advancement in precision. This improvement not only provides more accurate diagnoses but also enables more personalized and effective treatment plans. For patients, this enhanced accuracy translates to better prognostic assessments and tailored treatments, potentially leading to improved outcomes and reduced likelihood of overtreatment or under treatment. For medical staff, the improved accuracy reduces the likelihood of misdiagnoses and enhances workflow efficiency by minimizing manual interpretation, thus alleviating some of the burdens associated with cancer staging. The model's performance is optimized through various testing methods and statistical evaluations, validating its stability and reliability. The integration of edge detection and SOMs captures comprehensive information, prevents overfitting, and provides valuable insights into data clustering. This combined approach supports personalized medicine by ensuring treatments are customized to individual patient characteristics, ultimately contributing to better survival rates and quality of life for patients. Breast cancer staging Hybrid Convolutional Neural Network (CNN) Self-organizing Map Edge detection Medical imaging Duke Breast Cancer MRI dataset 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. 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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-4794714","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336576618,"identity":"365f829a-9faa-4b29-bd99-1ef3030503b4","order_by":0,"name":"Naim Ajlouni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYFACxgYgwSYDYh74wMAAZjDwEKEFrObgDAYDHpgWCUJ2gVUy8xCjxZz9cOsGhho+Hn6J3IOHbWr+8PD3H2B88LaNoc68AbsWy57EthsMx9h4JGfkJRzOOWbAI3EjgdlwbhuDhMwB7FoMDoC0sLHxGNzIMTicwwZ0GJArzQvUgstlBucfArX8g2qx+GfAI3/+APtvvFpuAG1hbINqYWwz4DE4kMDGjF8L0JbEPqBfet4YHOztM+YxvJHYLDnnnITkDJwOS39248O3Y3L87DnGH358k5OTO3/44Ic3ZTb8eCMmgeEYMhccuQRjsoaQglEwCkbBKBjJAADMhVTIwBMyYQAAAABJRU5ErkJggg==","orcid":"","institution":"Istanbul Atlas University","correspondingAuthor":true,"prefix":"","firstName":"Naim","middleName":"","lastName":"Ajlouni","suffix":""},{"id":336576619,"identity":"6371c51b-74ef-4d82-861c-2d621eacb34c","order_by":1,"name":"Adem Özyavaş","email":"","orcid":"","institution":"Istanbul Atlas University","correspondingAuthor":false,"prefix":"","firstName":"Adem","middleName":"","lastName":"Özyavaş","suffix":""},{"id":336576620,"identity":"a7135673-2f53-402f-ba7b-183a6f4e4902","order_by":2,"name":"Firas Ajlouni","email":"","orcid":"","institution":"Lancashire college of Further Education","correspondingAuthor":false,"prefix":"","firstName":"Firas","middleName":"","lastName":"Ajlouni","suffix":""},{"id":336576621,"identity":"8c279a04-efd8-4e1b-9c87-88eb1b14d744","order_by":3,"name":"Mustafa Takaoğlu","email":"","orcid":"","institution":"Scientific and Technological Research Council of Turkey","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Takaoğlu","suffix":""},{"id":336576622,"identity":"17d56ff4-a570-44d9-af95-5bb65cfedb55","order_by":4,"name":"Faruk Takaoğlu","email":"","orcid":"","institution":"Scientific and Technological Research Council of Turkey","correspondingAuthor":false,"prefix":"","firstName":"Faruk","middleName":"","lastName":"Takaoğlu","suffix":""}],"badges":[],"createdAt":"2024-07-24 10:46:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4794714/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4794714/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61923293,"identity":"ad7bff99-5699-4344-9c6f-b2d8310e231e","added_by":"auto","created_at":"2024-08-07 06:31:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":764317,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancingTNMStaginginBreastCancerAHybridApproachwithCNNEdgeDetectionandSelfOrganizingMapsforImprovedAccuracy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4794714/v1_covered_f702647d-d4a0-4c81-8df4-9fae5196dee3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy","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":false,"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 staging, Hybrid Convolutional Neural Network (CNN), Self-organizing Map, Edge detection, Medical imaging, Duke Breast Cancer MRI dataset","lastPublishedDoi":"10.21203/rs.3.rs-4794714/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4794714/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer remains a leading cause of mortality among women globally, underscoring the urgent need for improved diagnostic and staging techniques to enhance patient outcomes. This study aims to automate the TNM staging of breast cancer using a hybrid approach that integrates Convolutional Neural Networks (CNNs), edge detection methods, and Self-Organizing Maps (SOMs). Utilizing the Duke Breast Cancer MRI dataset, which provides detailed MRI scans crucial for accurate tumor characterization, the research addresses the limitations of traditional TNM staging, which often relies on manual interpretation by radiologists and can lead to inconsistencies and inaccuracies. Our approach combines CNNs with advanced edge detection algorithms and SOMs to automate and enhance the accuracy of breast cancer staging. The hybrid model effectively identifies and delineates tumor boundaries and critical anatomical features, offering a more reliable and objective evaluation. Notably, this method improves accuracy from 93% with conventional CNN models to 98%, representing a significant advancement in precision. 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