Advancing Early Disease Detection through Convolutional Neural Network Architectures in Medical Image Analysis

Advancing Early Disease Detection through Convolutional Neural Network Architectures in Medical Image Analysis | 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 Advancing Early Disease Detection through Convolutional Neural Network Architectures in Medical Image Analysis Thayer Morrill, Yunuo Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7648335/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 Alzheimer's disease (AD) is a common neurodegenerative disease, and its early detection is of great significance for delaying the progression of the disease and improving the quality of life of patients. Traditional clinical diagnostic methods rely on neuropsychological tests and imaging analysis, which are highly subjective, costly, and time-consuming. In recent years, convolutional neural networks (CNNs) have shown great potential in the field of medical image analysis due to their excellent feature extraction capabilities. This study proposes a CNN-based method for early detection of Alzheimer's disease. A deep learning model is trained using MRI imaging data to automatically learn lesion-related features, and classification experiments are performed. By comparing the performance of different CNN structures such as VGG, ResNet, and DenseNet, as well as the effects of 5-fold and 10-fold cross-validation strategies on the generalization ability of the model, the experimental results show that DenseNet121 performs best in the classification task, and 10-fold cross-validation can improve the stability of the model. The research results verify the feasibility of CNN in early detection of Alzheimer's disease, and provide research directions for combining multimodal data and optimizing deep learning models in the future. Alzheimer's disease convolutional neural network MRI image analysis deep learning Full Text Additional Declarations The authors declare no competing interests. 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-7648335","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517011027,"identity":"58cba5f2-43c5-4356-94a5-799601920c31","order_by":0,"name":"Thayer Morrill","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Thayer","middleName":"","lastName":"Morrill","suffix":""},{"id":517011028,"identity":"6907e6e2-1aaf-4a37-91bf-3c3ba93ee257","order_by":1,"name":"Yunuo Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYPACGx429gYgbWBBtJY0OT6eAyAtEkRrOWwsJ5EAYhChxeBG7jGJDzWHE9skn1/d8KNAgoG/vTuBgJa8NMkZx9IT26Rzym72AB0mcebsBgJacsykeRusQVrSbvAAtRhI5BKlhRnosDNpN/+QoMXZmE2C/dhtomyRPPPG2HLGsTQ5Np4cttsyBhI8BP3CdzzH8MaHGhse+fbjz26++WMjx9/ei1+LwgE4k8cATOJVDgLyDXAm+wOCqkfBKBgFo2BkAgApzkSh/L8tjQAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yunuo","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-09-18 10:11:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7648335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7648335/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91696615,"identity":"e22f6594-9814-4818-8b36-02f94232726c","added_by":"auto","created_at":"2025-09-19 09:40:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":402136,"visible":true,"origin":"","legend":"","description":"","filename":"AdvancingEarlyDiseaseDetectionthroughConvolutionalNeuralNetworkArchitecturesinMedicalImageAnalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7648335/v1_covered_311b1fcb-5fba-4f53-842f-b6d0b327613b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdvancing Early Disease Detection through Convolutional Neural Network Architectures in Medical Image Analysis\u003c/p\u003e","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":"Alzheimer's disease, convolutional neural network, MRI image analysis, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-7648335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7648335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer's disease (AD) is a common neurodegenerative disease, and its early detection is of great significance for delaying the progression of the disease and improving the quality of life of patients. 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