Fine-Tuned Deep Learning Architectures for Accurate Prostate Cancer Detection

Fine-Tuned Deep Learning Architectures for Accurate Prostate 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 Fine-Tuned Deep Learning Architectures for Accurate Prostate Cancer Detection Pedapudi Vijaya Bhaskar, Nilamani Bhoi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7777799/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 Prostate cancer is one of the most common diseases that affects men after the age of 60 years. Still, nowadays men after 40 years have to take several prevention methods to predict and recognise prostate cancer in the early stages. Many existing approaches have already been developed and used to detect prostate cancer using Prostate CT scan images. This paper presents the Fine-Tuned Learning Architecture (FTLA) approach, which contains multiple models that detect and classify normal and abnormal regions in prostate CT scan images. The pre-trained model, Multi-Layered ResNet101 with transfer learning, plays a significant role in identifying the accurate patterns of Prostate cancer regions. To improve the input CT scan image in terms of contrast and smoothing, firstly Contrast-Limited Adaptive Histogram Equalization (CLAHE) and ADF filtering is applied to obtain the refined image, followed by feature extraction techniques such as Histogram of Oriented Gradients (HOG) and Variational Autoencoders (VAE) extracts the edge, shape and latent features which shows huge impact on classification of prostate cancer. In the final stage, the refined model, Multi-Attention U-Net (MA-UNet), was used and integrated with the Fine-Tuned Multi-Layered Classifier (FTMLC) to classify accurate CT scan images. Researchers consider two datasets: one from Kaggle, collected from online sources, and the second, real-time CT scan images. The results show that the proposed approach achieves superior performance. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Prostate cancer Deep Learning (DL) Fine-Tuned Learning Architecture (FTLA) Contrast-Limited Adaptive Histogram Equalization (CLAHE) Anisotropic Diffusion Filtering (ADF) Histogram of Oriented Gradients (HOG) 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-7777799","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":554579075,"identity":"5287f965-3816-4516-8a6d-487d34e43e80","order_by":0,"name":"Pedapudi Vijaya Bhaskar","email":"data:image/png;base64,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","orcid":"","institution":"Veer Surendra Sai University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Pedapudi","middleName":"Vijaya","lastName":"Bhaskar","suffix":""},{"id":554579076,"identity":"6fb2e51b-024d-4532-84ca-f59681027c36","order_by":1,"name":"Nilamani Bhoi","email":"","orcid":"","institution":"Veer Surendra Sai University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Nilamani","middleName":"","lastName":"Bhoi","suffix":""}],"badges":[],"createdAt":"2025-10-04 06:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7777799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7777799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97436733,"identity":"aea0ce83-540a-4caa-9f4b-d84f310a4ecc","added_by":"auto","created_at":"2025-12-04 11:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1260387,"visible":true,"origin":"","legend":"","description":"","filename":"FineTunedDeepLearningArchitecturesforAccurateProstateCancerDetectionRevisedd1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7777799/v1/e0be5848a063bd010b629267.pdf"},{"id":97436732,"identity":"d912a2ca-99e5-4bb3-9dcc-2673d864fc40","added_by":"auto","created_at":"2025-12-04 11:14:14","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4694,"visible":true,"origin":"","legend":"","description":"","filename":"b270464a62394045a9c516bb9d98b149.json","url":"https://assets-eu.researchsquare.com/files/rs-7777799/v1/16523f7e12df6263ea5eef4a.json"},{"id":100549043,"identity":"67a4c7e9-8979-4b2e-9292-f4df288c959a","added_by":"auto","created_at":"2026-01-19 08:22:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":767547,"visible":true,"origin":"","legend":"","description":"","filename":"FineTunedDeepLearningArchitecturesforAccurateProstateCancerDetectionRevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7777799/v1_covered_0afc8688-058d-4d7e-a67a-a174fe1b7e9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fine-Tuned Deep Learning Architectures for Accurate Prostate Cancer Detection","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":"Prostate cancer, Deep Learning (DL), Fine-Tuned Learning Architecture (FTLA), Contrast-Limited Adaptive Histogram Equalization (CLAHE), Anisotropic Diffusion Filtering (ADF), Histogram of Oriented Gradients (HOG)","lastPublishedDoi":"10.21203/rs.3.rs-7777799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7777799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Prostate cancer is one of the most common diseases that affects men after the age of 60 years. 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