A novel predictive model decision system for the detection and classification of congenital heart disease and respiratory diseases from chest X-ray images based on IoT and cloud

A novel predictive model decision system for the detection and classification of congenital heart disease and respiratory diseases from chest X-ray images based on IoT and cloud | 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 A novel predictive model decision system for the detection and classification of congenital heart disease and respiratory diseases from chest X-ray images based on IoT and cloud Madhumita Pal, Subhankit prusti, Rajiv lochan Bal, Sujata Dash, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7597193/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Congenital heart diseases (CHDs), including Ventricular Septal Defect (VSD), Atrial Septal Defect (ASD), and Patent Ductus Arteriosus (PDA), are among the most prevalent birth-related anomalies in children, often requiring early diagnosis to prevent severe complications. Similarly, respiratory diseases such as tuberculosis, pneumonia, and COVID-19 continue to pose significant public health challenges globally. Early and precise identification of these conditions through chest X-ray analysis plays a crucial role in supporting accurate diagnosis and guiding treatment strategies. This research presents an automated deep learning framework designed to classify congenital heart diseases (CHDs) and respiratory disorders by utilizing two distinct chest X-ray datasets. To capture and learn discriminative image features, advanced models, including Vision Transformer (ViT), ConvMixer, and VGG-19, were applied. The models were trained and evaluated on labeled datasets containing paediatric chest X-rays for CHD detection and mixed adult/paediatric chest X-rays for respiratory disease classification. The performance of each model was assessed using metrics such as accuracy(acc), precision(pr), recall(re), F1-score, Area under the curve (AUC), and Precision Recall(P-R) curves. Experimental results demonstrate that the Vision Transformer outperforms traditional convolution-based models, achieving superior classification accuracy across all disease categories. Based on our findings, the ViT model outperformed the other two models across both datasets. For Dataset-1, the ViT model achieved an average acc of 0.86, pr of 0.83, re of 0.71, an F1-score of 0.76, and an AUC of 0.80. For Dataset-2, the model achieved an acc of 0.93, a pr of 0.89, a re of 0.88, an F1-score of 0.87, and an AUC of 0.89. This work emphasizes the effectiveness of transformer-based models in the field of healthcare imaging analysis tasks and their applicability in real-time healthcare environments through integration with IoT and cloud platforms. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Congenital heart disease accuracy precision recall F1-score Vision transformer IoT Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 07 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 06 Jan, 2026 Editor invited by journal 18 Sep, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 17 Sep, 2025 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-7597193","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633622925,"identity":"cf364c62-0672-4106-901a-960c023db622","order_by":0,"name":"Madhumita Pal","email":"","orcid":"","institution":"Government College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Madhumita","middleName":"","lastName":"Pal","suffix":""},{"id":633622926,"identity":"d2e89827-e48d-4aa7-b199-12ad7ade1904","order_by":1,"name":"Subhankit prusti","email":"","orcid":"","institution":"Odisha University of Technology and Research","correspondingAuthor":false,"prefix":"","firstName":"Subhankit","middleName":"","lastName":"prusti","suffix":""},{"id":633622927,"identity":"01023998-68ef-428f-b686-04a5e9006a7a","order_by":2,"name":"Rajiv lochan Bal","email":"","orcid":"","institution":"Orissa School of Mining EngineeringDepartment of Mining EngineeringDepartment of Mining Engineering","correspondingAuthor":false,"prefix":"","firstName":"Rajiv","middleName":"lochan","lastName":"Bal","suffix":""},{"id":633622928,"identity":"bbe61ba1-db62-4ded-819a-d65dc32df1d1","order_by":3,"name":"Sujata Dash","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYBAC+QaGBCBlY8cPpJkZKoBsZuYGvFoMDgCVHmBIS5ZsAGk5A9LCSEALiDjAcJhxwwGgFsY2EJeQFomEh58/1DAzS7YnP/5cOK82mr8dqOVHxTbcfpmRkCxx4BgbHz/PMzPpmduO5844zNjA2HPmNm5rbiQkSBxg42GWnJFgxsy77VhuA1AL0IV4tST/OPBPgnHDjfTPn3nnHMudT4SWNImDbQZALTkG0rwNNbkbCGkxOPMgzeJsX0KyZM+bMmmeYwdyNwK1HMTnF/n2nOQbFd/+2/Gzp2/+zFNTlzvv/OGDD35U4HEYA08CMu8wmDyARz0QsKPI1+FXPApGwSgYBSMSAABo2WOn6/EeJgAAAABJRU5ErkJggg==","orcid":"","institution":"Nagaland University","correspondingAuthor":true,"prefix":"","firstName":"Sujata","middleName":"","lastName":"Dash","suffix":""},{"id":633622929,"identity":"083bb631-47d0-46db-bdb3-97d480ce4198","order_by":4,"name":"Ganapati Panda","email":"","orcid":"","institution":"C.V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Ganapati","middleName":"","lastName":"Panda","suffix":""},{"id":633622930,"identity":"0b5d3930-f2f2-42c3-832e-c954f694f2fc","order_by":5,"name":"Zhen Wang","email":"","orcid":"","institution":"Westlake University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":633622931,"identity":"a1f7180a-abc3-40a1-921a-432c4e308dbd","order_by":6,"name":"Haipeng Liu","email":"","orcid":"","institution":"Coventry University","correspondingAuthor":false,"prefix":"","firstName":"Haipeng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-09-12 06:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7597193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7597193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108806342,"identity":"9ab79dc7-3ba4-4702-ab1e-86615a99be18","added_by":"auto","created_at":"2026-05-08 15:28:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1241799,"visible":true,"origin":"","legend":"","description":"","filename":"congenitalHDrevised15.8.2025HL3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7597193/v1_covered_fd08e8bb-3e3a-4013-9809-ddf9b0562b33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel predictive model decision system for the detection and classification of congenital heart disease and respiratory diseases from chest X-ray images based on IoT and cloud","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Congenital heart disease, accuracy, precision, recall, F1-score, Vision transformer, IoT","lastPublishedDoi":"10.21203/rs.3.rs-7597193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7597193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Congenital heart diseases (CHDs), including Ventricular Septal Defect (VSD), Atrial Septal Defect (ASD), and Patent Ductus Arteriosus (PDA), are among the most prevalent birth-related anomalies in children, often requiring early diagnosis to prevent severe complications. Similarly, respiratory diseases such as tuberculosis, pneumonia, and COVID-19 continue to pose significant public health challenges globally. Early and precise identification of these conditions through chest X-ray analysis plays a crucial role in supporting accurate diagnosis and guiding treatment strategies. This research presents an automated deep learning framework designed to classify congenital heart diseases (CHDs) and respiratory disorders by utilizing two distinct chest X-ray datasets. To capture and learn discriminative image features, advanced models, including Vision Transformer (ViT), ConvMixer, and VGG-19, were applied. The models were trained and evaluated on labeled datasets containing paediatric chest X-rays for CHD detection and mixed adult/paediatric chest X-rays for respiratory disease classification. The performance of each model was assessed using metrics such as accuracy(acc), precision(pr), recall(re), F1-score, Area under the curve (AUC), and Precision Recall(P-R) curves. Experimental results demonstrate that the Vision Transformer outperforms traditional convolution-based models, achieving superior classification accuracy across all disease categories. Based on our findings, the ViT model outperformed the other two models across both datasets. For Dataset-1, the ViT model achieved an average acc of 0.86, pr of 0.83, re of 0.71, an F1-score of 0.76, and an AUC of 0.80. For Dataset-2, the model achieved an acc of 0.93, a pr of 0.89, a re of 0.88, an F1-score of 0.87, and an AUC of 0.89. This work emphasizes the effectiveness of transformer-based models in the field of healthcare imaging analysis tasks and their applicability in real-time healthcare environments through integration with IoT and cloud platforms.","manuscriptTitle":"A novel predictive model decision system for the detection and classification of congenital heart disease and respiratory diseases from chest X-ray images based on IoT and cloud","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 10:04:53","doi":"10.21203/rs.3.rs-7597193/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-07T05:17:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T13:22:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297464469751583644355780862104519155810","date":"2026-05-03T13:18:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256857808709131751802845534621110176176","date":"2026-05-01T12:55:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67000639280052159215379197673411620119","date":"2026-04-29T04:06:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T12:23:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39659509927752436993722331925586670824","date":"2026-04-28T12:09:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48676807261787610979411878573543444018","date":"2026-04-28T12:01:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T11:36:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T12:47:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T09:39:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T05:02:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-17T04:58:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5f0ada8-fe2c-4dda-838b-fe3792813e3d","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-07T05:17:50+00:00","index":92,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T13:22:23+00:00","index":91,"fulltext":""},{"type":"reviewerAgreed","content":"297464469751583644355780862104519155810","date":"2026-05-03T13:18:02+00:00","index":90,"fulltext":""},{"type":"reviewerAgreed","content":"256857808709131751802845534621110176176","date":"2026-05-01T12:55:06+00:00","index":89,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67437784,"name":"Health sciences/Cardiology"},{"id":67437785,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":67437786,"name":"Health sciences/Diseases"},{"id":67437787,"name":"Health sciences/Health care"},{"id":67437788,"name":"Physical sciences/Mathematics and computing"},{"id":67437789,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-07T10:04:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 10:04:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7597193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7597193","identity":"rs-7597193","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}