A Multi-level Deep Neural Network to Diagnose Coronavirus Disease with Imbalanced Data

A Multi-level Deep Neural Network to Diagnose Coronavirus Disease with Imbalanced Data | 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 A Multi-level Deep Neural Network to Diagnose Coronavirus Disease with Imbalanced Data Iman Zabbah, Kamran Layeghi, Reza Ebrahimpour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4542329/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 One of the main challenges of medical data mining is the classification of imbalanced datasets. Often the data which are used for training classification is of no proper distribution. It occurs when a class has few samples in nature inclining the learner model to the major class. This imbalance is quite obvious in the data X-ray image of coronavirous, due to its newness, so the samples of healthy and pneumonia are more than COVID- 19 ones. Here we proposed a multi-level model that improves diagnosis of the disease using data augmentation in the minor class of coronavirous cases. The model contains a deep feature extractor, a novel algorithem to find the scatter topolgy of the minor class, a mechanism to selectively generate synthetic samples and finally clasify input data. Coronavirous images are fed to a deep neural networks as a feature estractor. Then, finding scatter topology of the minor class, synthetic samples are generated in the feature space and evaluated by some expertes so that the synthetic samples resemble real ones mostly. Finally, the pool of synthetic and real samples of each classes are transferred to a discriminator network after the dataset is balanced. Results indicate that the production of samples by the framework not only improves classification performance but also has a more desirable performance than other data augmentation methods. Deep learning Imbalanced Dataset Coronavirous Classification synthetic data generation 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-4542329","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312654090,"identity":"8c12d750-d40b-4abd-a604-7c536d66d61d","order_by":0,"name":"Iman Zabbah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACNhDB2HCAwYC9sQEixEy0Fp6DRGphgGuRSCDSYXxiB5g//NxxR95c8nHjZx4GO3kGdt4H+B0mncBg2HvmmeHO2YnN0jwMyYYNzOwGBLUk8LYdZtxwO7EBqIU5gYGZDb/DQFoO/m07bL/h5sHm3zwM9URpYWwG2pK44QZjG9CWw8RoSWxmlj3zLHnDmcQ2yzkGxw3bCGmRn518+OPbHXdsNxw//vjGm4pqeX7+Y/i1gCIFiWMAjdxRMApGwSgYBZQBADAwP6sOHiirAAAAAElFTkSuQmCC","orcid":"","institution":"Islamic Azad University","correspondingAuthor":true,"prefix":"","firstName":"Iman","middleName":"","lastName":"Zabbah","suffix":""},{"id":312654092,"identity":"125c9c71-effd-48b6-929c-b167d5330a78","order_by":1,"name":"Kamran Layeghi","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Kamran","middleName":"","lastName":"Layeghi","suffix":""},{"id":312654093,"identity":"57d1b96a-55e3-4193-85cd-96194160d830","order_by":2,"name":"Reza Ebrahimpour","email":"","orcid":"","institution":"Sharif University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Ebrahimpour","suffix":""}],"badges":[],"createdAt":"2024-06-06 20:18:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4542329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4542329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87549876,"identity":"cb17dd0a-37a1-429a-86c2-d6173ff3b8b6","added_by":"auto","created_at":"2025-07-25 06:01:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":950314,"visible":true,"origin":"","legend":"","description":"","filename":"1412023ManusciptFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4542329/v1_covered_5c508eec-8101-47c5-ad3d-4afabf206775.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-level Deep Neural Network to Diagnose Coronavirus Disease with Imbalanced Data","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":"Deep learning, Imbalanced Dataset, Coronavirous, Classification, synthetic data generation","lastPublishedDoi":"10.21203/rs.3.rs-4542329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4542329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"One of the main challenges of medical data mining is the classification of imbalanced datasets. 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