An Advanced Infrared-Based Thoracic Disease Detection Framework Using Vision Transformer-Enabled Extended Convolutional Neural Networks

An Advanced Infrared-Based Thoracic Disease Detection Framework Using Vision Transformer-Enabled Extended Convolutional Neural Networks | 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 Advanced Infrared-Based Thoracic Disease Detection Framework Using Vision Transformer-Enabled Extended Convolutional Neural Networks Ahed Abugabah, Ahmad Ali Alzubi, Hannoud Al Moghrabi, Piyush Kumar Shukla, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8289629/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 Chest X-ray (CXR) imaging is a prevalent and cost-effective way to find thoracic disorders such pneumonia, TB, and cardiomegaly. Even though CXRs are used a lot in medicine, it is still hard to read them correctly since it takes a lot of skill and is often sensitive to differences between readers. Deep learning-based computer-aided detection (CAD) systems have garnered interest for their capacity to aid radiologists by automating the identification and categorization of chest illnesses, therefore addressing these constraints. While deep learning has greatly improved the processing of medical images, many current models for finding chest diseases only use Convolutional Neural Networks (CNNs). These models might not fully reflect global contextual linkages, which makes them less useful. The NIH Chest X-ray dataset, which is often used to train models, has a lot of spatial information that regular CNNs might not be able to use to its full potential. There is still a pressing demand for strong diagnostic models that successfully integrate local and global feature extraction to enhance classification accuracy and generalizability. This paper offers a hybrid deep learning architecture that integrates Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to diagnose chest disorders utilizing the NIH Chest X-ray dataset, which comprises 112,120 labeled pictures from 30,805 individuals. CNNs help the model see spatial characteristics, and ViTs help it see contextual connections. Generative Adversarial Networks (GANs) are used to add more data, making it more diverse and stronger. Using NLP-derived labels for weakly-supervised learning and sophisticated augmentation using transfer learning makes the model work even better. The suggested CNN-ViT model does better than older architectures in terms of accuracy, precision, recall, and F1-score. It shows better diagnostic capacity and generalization, which means it might be very useful for automated chest illness identification in a clinical setting. Chest X-ray Computer-aided detection and diagnosis (CAD) National Institutes of Health (NIH) Convolutional Neural Network (CNN) Vision Transformer (ViT) 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-8289629","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565557746,"identity":"25631a14-7fac-458a-a049-201ef0e248dc","order_by":0,"name":"Ahed Abugabah","email":"","orcid":"","institution":"Zayed University, United Arab Emirates (UAE)","correspondingAuthor":false,"prefix":"","firstName":"Ahed","middleName":"","lastName":"Abugabah","suffix":""},{"id":565557748,"identity":"603f9af4-d9ad-464e-9a14-ab3c49b048f3","order_by":1,"name":"Ahmad Ali Alzubi","email":"","orcid":"","institution":"Department of Computer Science, College of 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Networks","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":"Chest X-ray, Computer-aided detection and diagnosis (CAD), National Institutes of Health (NIH), Convolutional Neural Network (CNN), Vision Transformer (ViT)","lastPublishedDoi":"10.21203/rs.3.rs-8289629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8289629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChest X-ray (CXR) imaging is a prevalent and cost-effective way to find thoracic disorders such pneumonia, TB, and cardiomegaly. Even though CXRs are used a lot in medicine, it is still hard to read them correctly since it takes a lot of skill and is often sensitive to differences between readers. Deep learning-based computer-aided detection (CAD) systems have garnered interest for their capacity to aid radiologists by automating the identification and categorization of chest illnesses, therefore addressing these constraints. While deep learning has greatly improved the processing of medical images, many current models for finding chest diseases only use Convolutional Neural Networks (CNNs). These models might not fully reflect global contextual linkages, which makes them less useful. The NIH Chest X-ray dataset, which is often used to train models, has a lot of spatial information that regular CNNs might not be able to use to its full potential. There is still a pressing demand for strong diagnostic models that successfully integrate local and global feature extraction to enhance classification accuracy and generalizability. This paper offers a hybrid deep learning architecture that integrates Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to diagnose chest disorders utilizing the NIH Chest X-ray dataset, which comprises 112,120 labeled pictures from 30,805 individuals. CNNs help the model see spatial characteristics, and ViTs help it see contextual connections. Generative Adversarial Networks (GANs) are used to add more data, making it more diverse and stronger. Using NLP-derived labels for weakly-supervised learning and sophisticated augmentation using transfer learning makes the model work even better. The suggested CNN-ViT model does better than older architectures in terms of accuracy, precision, recall, and F1-score. It shows better diagnostic capacity and generalization, which means it might be very useful for automated chest illness identification in a clinical setting.\u003c/p\u003e","manuscriptTitle":"An Advanced Infrared-Based Thoracic Disease Detection Framework Using Vision Transformer-Enabled Extended Convolutional Neural Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 06:48:42","doi":"10.21203/rs.3.rs-8289629/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a59b29ce-add2-4441-865a-03f52eaf51a1","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T00:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 06:48:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8289629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8289629","identity":"rs-8289629","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}