An Analysis of Multi-Criteria Performance in Deep Learning-Based Medical Image Classification: A comprehensive review

An Analysis of Multi-Criteria Performance in Deep Learning-Based Medical Image Classification: A comprehensive review | 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 An Analysis of Multi-Criteria Performance in Deep Learning-Based Medical Image Classification: A comprehensive review Samridhi Hisaria, Pratham Sharma, Raksha Gupta, Konatham Sumalatha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4125301/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 Objective: Investigate the potential of deep learning, specifically Convolutional Neural Networks (CNNs), for disease detection and classification in medical image analysis. Methods and procedures: Review various CNN architectures (ResNet, EfficientNet, MobileNet, VGG, DenseNet) on retinal fundus and brain tumor datasets, evaluating performance through loss, AUROC, accuracy, and precision. Results: Retinal fundus: ResNet 152 achieved highest accuracy and precision, but also highest loss. Precisions indicate potential for improvement. Brain tumor: VGG 16 achieved highest accuracy and precision, but EfficientNet models displayed poor differentiation between positive and negative classes. CheXpert: MobileNet demonstrated best overall performance. No single model consistently outperformed others across all datasets. Conclusion: Deep learning shows promise for medical image analysis, with varied performances across different architectures and datasets. No single ”best” model exists, necessitating careful selection based on specific disease and data characteristics. Precision needs improvement in many cases, highlighting an area for further research. EfficientNet models require further investigation for brain tumor analysis. Clinical and Translational Impact Statement: The paper explores transfer learning’s impact on translating deep learning for medical image analysis, potentially boosting clinical impact. By leveraging pre-trained models and overcoming data limitations, it aims to improve accuracy, efficiency, andgeneralizability, paving the way for real-world medical applications. Convolutional neural networks (CNNs) Disease detection Disease classification Clinical applications 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-4125301","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281173979,"identity":"ecb6d4c6-d24f-4998-8642-9f3167b618f5","order_by":0,"name":"Samridhi Hisaria","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Samridhi","middleName":"","lastName":"Hisaria","suffix":""},{"id":281173980,"identity":"34fbd295-a228-4c47-a695-28ded30bd77a","order_by":1,"name":"Pratham Sharma","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Pratham","middleName":"","lastName":"Sharma","suffix":""},{"id":281173981,"identity":"297d0a49-d7dd-4c8c-8d13-f8f4a7b87256","order_by":2,"name":"Raksha Gupta","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Raksha","middleName":"","lastName":"Gupta","suffix":""},{"id":281173982,"identity":"201c11e4-bc21-4e3c-aca9-b4d337e17217","order_by":3,"name":"Konatham Sumalatha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJUlEQVRIie3OPUvDQBjA8ScE6hLI+oSKfoWnFKNCrR/EJRLolAMnUQo202WJdO3HCAjFMUeGLNGuCV0iQqcuIkgGB6+gBfVacHO4/3BwL7+7A9Dp/mG4Gozwa3oBQGCm4MH39V9ErLcIkKDlgef9jVgEn88oc6Jb8fpy3z8DuzhIGurfHO4Ub/jUwL4dmrxWkLb16KMofBZi4FYx+XgcsynKj3UmqRGRguxhQCi4KYnllhaZSCm7I0mMBAyOatJtBB+x0C7c6p1GSLPl84qcbiJt+R/5SsZCCNy5RRlSGRi1JOebiBMX7tEDzxnHweV8l3InKRed2hugP8nUBPO4W17zIRvb2bRaXg1tmvm1aHq9k3EULVRkXevnXQDmtvM6nU6n29YHJupkolaFGLgAAAAASUVORK5CYII=","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Konatham","middleName":"","lastName":"Sumalatha","suffix":""}],"badges":[],"createdAt":"2024-03-18 18:17:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4125301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4125301/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53845553,"identity":"18b0d8dc-ae4e-4a7b-8e07-32ba89647f33","added_by":"auto","created_at":"2024-04-01 08:30:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":656845,"visible":true,"origin":"","legend":"","description":"","filename":"AnAnalysisofMultiCriteriaPerformanceinDeepLearningBasedMedicalImageClassification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4125301/v1_covered_6fcccfb3-06cd-41ff-a324-3afeab778d49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Analysis of Multi-Criteria Performance in Deep Learning-Based Medical Image Classification: A comprehensive review","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":"Convolutional neural networks (CNNs), Disease detection, Disease classification, Clinical applications","lastPublishedDoi":"10.21203/rs.3.rs-4125301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4125301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: Investigate the potential of deep learning, specifically Convolutional Neural Networks (CNNs), for disease detection and classification in medical image analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods and procedures: Review various CNN architectures (ResNet, EfficientNet, MobileNet, VGG, DenseNet) on retinal fundus and brain tumor datasets, evaluating performance through loss, AUROC, accuracy, and precision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Retinal fundus: ResNet 152 achieved highest accuracy and precision, but also highest loss. Precisions indicate potential for improvement. Brain tumor: VGG 16 achieved highest accuracy and precision, but EfficientNet models displayed poor differentiation between positive and negative classes. CheXpert: MobileNet demonstrated best overall performance. No single model consistently outperformed others across all datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Deep learning shows promise for medical image analysis, with varied performances across different architectures and datasets. No single ”best” model exists, necessitating careful selection based on specific disease and data characteristics. Precision needs improvement in many cases, highlighting an area for further research. 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