A Systematic Analysis Using DNN Based Breast Cancer Identification and Stage Classification Model

A Systematic Analysis Using DNN Based Breast Cancer Identification and Stage Classification Model | 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 Systematic Analysis Using DNN Based Breast Cancer Identification and Stage Classification Model Umamageswari Arasakumaran, Deepa Sivapatham, Sherin Beevi Lucas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4569501/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 The increased incidence of breast cancer on a global level is an important problem in public health, emphasizing the need for rapid and effective techniques for diagnosing the cancer at its earliest stages. This study offers an innovative Deep Neural Network (DNN) technique for identifying and categorizing breast cancer. It utilizes advanced methods of machine learning to improve its precision and efficacy. This study offers an in-depth examination of DNNs for their use of detecting breast cancer. This work concentrates on the DNNs' ability to identify complicated patterns within mammographic images, with the goal of enhancing the accuracy of detection. The proposed DNN design incorporates several levels of abstraction, taking use of the organization possibilities of neural networks. Convolutional layers collect local data, whereas densely associated layers capture global patterns, allowing the algorithm to identify subtle problems that indicate tumors in breasts. The design of the model is optimized by further training on different data sets, guaranteeing its ability to respond to the inherent variations in tissue makeup and lesion characteristics. To attempt to address the challenges related to a lack of data annotations, transfer learning techniques are employed. These methods leverage the knowledge gained from models that have been trained on large datasets. The success rate of the proposed DNN model for identifying and categorizing breast cancer is thoroughly assessed on standard datasets, through assessing its performance with conventional methods and recent algorithms. The model's superior in providing reliable and precise diagnostic results can be seen through the use of assessment standards like the accuracy and confusion matrix. The model suggested has strong diagnostic accuracy, offering an appropriate strategy for early and efficient identification of breast cancers. DNN Breast cancer prediction and classification Threshold based segmentation Contrast Limited Adaptive Histogram Equalization (CLAHE) 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-4569501","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320740336,"identity":"f98f526b-36b4-44b7-9a96-3e2a458b657a","order_by":0,"name":"Umamageswari Arasakumaran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDADfhCRwAAlE4jRItkAVmxAghaDAxCKgaB6g2uHn2748ctG3vj82acbHvz5w8DPnmPA8HAHHi2308xu9valGW67kW52I7HNgEGy540BQ+IZfFoSzG7w9hxm3HaDje1GYoMBg8ENoC2Jbfi0pH+7+bfnv/3m/mNsNxL+GDDYE9aSY3ab58eBxA0MaUAtbEBbJAhokbydU3ZbtiE5ecYNoJbENmMeiTPPCg7g08J3O33bzTd/7Gz7gQ67+eOPnBx/e/LGhz/xaAEDRiQFPCDiAAENQPCHsJJRMApGwSgYwQAA2fVaXO+AWHMAAAAASUVORK5CYII=","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Umamageswari","middleName":"","lastName":"Arasakumaran","suffix":""},{"id":320740337,"identity":"99206fe1-708c-43b9-af58-2570ba89895d","order_by":1,"name":"Deepa Sivapatham","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Deepa","middleName":"","lastName":"Sivapatham","suffix":""},{"id":320740338,"identity":"34a8997f-d532-4e70-b0d8-c8a3045e9115","order_by":2,"name":"Sherin Beevi Lucas","email":"","orcid":"","institution":"R.M.D Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Sherin","middleName":"Beevi","lastName":"Lucas","suffix":""},{"id":320740339,"identity":"1036a815-b5f8-4715-9b29-d7b88f88464a","order_by":3,"name":"Vasukidevi Gurusamy","email":"","orcid":"","institution":"Sri Venkateswara College of Technology","correspondingAuthor":false,"prefix":"","firstName":"Vasukidevi","middleName":"","lastName":"Gurusamy","suffix":""},{"id":320740340,"identity":"c49da006-0968-4421-a6ed-9b84ad7eb1b9","order_by":4,"name":"Sangari Arasakumaran","email":"","orcid":"","institution":"Rajalakshmi Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Sangari","middleName":"","lastName":"Arasakumaran","suffix":""}],"badges":[],"createdAt":"2024-06-12 10:30:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4569501/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4569501/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59563714,"identity":"948f8fe6-ca26-426d-b261-32531c5a140f","added_by":"auto","created_at":"2024-07-03 08:45:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":563510,"visible":true,"origin":"","legend":"","description":"","filename":"ArtificialIntelligencereview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4569501/v1_covered_37a35647-263b-4126-b361-fe755bfacb4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Systematic Analysis Using DNN Based Breast Cancer Identification and Stage Classification Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"DNN, Breast cancer prediction and classification, Threshold based segmentation, Contrast Limited Adaptive Histogram Equalization (CLAHE)","lastPublishedDoi":"10.21203/rs.3.rs-4569501/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4569501/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The increased incidence of breast cancer on a global level is an important problem in public health, emphasizing the need for rapid and effective techniques for diagnosing the cancer at its earliest stages. 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