Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models

Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models | 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 Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models Balaji M, Venkata Arun Kumar C, Ayyasamy S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4322207/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Accurate diagnosis of COVID-19 is critical for patient management and disease control. In this study, we evaluate the performance of Convolutional Neural Network (CNN) models, including VGG, ResNet, Inception, and Xception, for COVID-19 detection using CT scans and chest X-ray images. Leveraging deep learning algorithms and multiple layers such as Conv2D, MaxPooling2D, Flatten, and Dense, we analyze medical images to identify COVID-19 patterns. Through comprehensive dataset training and evaluation, we assess model accuracy, sensitivity, and specificity. Our findings highlight the potential of CNN-based approaches for accurate COVID-19 diagnosis from chest radiography images, contributing to the development of advanced diagnostic tools in combating the pandemic. Neural Networks Resnet VGG Inception Xception Image Processing Convolutional Neural Nets Image Recognition Covid-19 Carcinoma CT Scan Chest X-Ray Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 Jun, 2024 Reviewers invited by journal 07 Jun, 2024 Editor assigned by journal 29 May, 2024 First submitted to journal 27 May, 2024 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-4322207","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311659900,"identity":"c266e59f-18b2-4af2-9bac-221f0426e96a","order_by":0,"name":"Balaji M","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-5200-6858","institution":"Vellore Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Balaji","middleName":"","lastName":"M","suffix":""},{"id":311659901,"identity":"fa5c8a69-f6a1-4545-adc5-634f36fa3622","order_by":1,"name":"Venkata Arun Kumar C","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Venkata","middleName":"Arun Kumar","lastName":"C","suffix":""},{"id":311659902,"identity":"9692f339-2e8f-4c1a-8920-d4098d575807","order_by":2,"name":"Ayyasamy S","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ayyasamy","middleName":"","lastName":"S","suffix":""}],"badges":[],"createdAt":"2024-04-25 07:10:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4322207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4322207/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58793012,"identity":"041b74ca-1e09-40e5-93de-d1086319bc75","added_by":"auto","created_at":"2024-06-21 07:43:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1517819,"visible":true,"origin":"","legend":"","description":"","filename":"MainDocument.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4322207/v1_covered_84432b33-4916-472c-9e76-026f0a72f16e.pdf"}],"financialInterests":"","formattedTitle":"Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neural Networks, Resnet, VGG, Inception, Xception, Image Processing, Convolutional Neural Nets, Image Recognition, Covid-19, Carcinoma, CT Scan, Chest X-Ray","lastPublishedDoi":"10.21203/rs.3.rs-4322207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4322207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate diagnosis of COVID-19 is critical for patient management and disease control. 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