Hybrid Quantum-Classical Neural Network for Automated Pneumonia Detection from Chest X-Rays: A Comparative Study

Hybrid Quantum-Classical Neural Network for Automated Pneumonia Detection from Chest X-Rays: A Comparative Study | 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 Hybrid Quantum-Classical Neural Network for Automated Pneumonia Detection from Chest X-Rays: A Comparative Study Dr. Senthilkumar K, 2. Dr. Sankar S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8940353/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 Pneumonia remains a leading cause of mortality globally, necessitating rapid and accurate diagnostic tools to assist radiologists. While classical Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), have demonstrated high efficacy in medical image analysis, they are often characterized by high computational complexity and massive parameter requirements. This research proposes a novel Hybrid Quantum-Classical Neural Network (HQCNN) to detect Pneumonia from chest X-ray images with enhanced computational efficiency. By integrating a classical ResNet-18 architecture for feature extraction with a Variational Quantum Circuit (VQC) based on PennyLane, we leverage the quantum mechanical principles of superposition and entanglement for pattern recognition. The proposed model was trained and validated on a standard chest X-ray dataset and benchmarked against a classical transfer learning equivalent. Experimental results indicate that the hybrid quantum model achieved a validation accuracy of 78% with significantly fewer trainable parameters (~ 24) compared to the classical baseline (62% accuracy, ~ 1026 parameters). Furthermore, the quantum model demonstrated superior sensitivity (100% Recall), successfully identifying all positive pneumonia cases in the validation batch. This study highlights the potential of Quantum Machine Learning (QML) as a resource-efficient, high-sensitivity alternative for future medical diagnostic systems. Quantum Machine Learning Pneumonia Detection Hybrid Neural Networks Variational Quantum Circuits PennyLane Medical Image Analysis 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-8940353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616498723,"identity":"28ac27f1-1a65-403f-9413-413c542d9214","order_by":0,"name":"Dr. Senthilkumar K","email":"data:image/png;base64,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","orcid":"","institution":"Rajalakshmi Engineering college","correspondingAuthor":true,"prefix":"Dr.","firstName":"Senthilkumar","middleName":"","lastName":"K","suffix":""},{"id":616498725,"identity":"927e4059-02dc-4a4f-bdc2-431ff6377159","order_by":1,"name":"2.\tDr. Sankar S","email":"","orcid":"","institution":"GRT Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"2.\tDr.","middleName":"Sankar","lastName":"S","suffix":""}],"badges":[],"createdAt":"2026-02-22 16:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8940353/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8940353/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109488197,"identity":"983d872d-4fb3-451f-a39a-e83d9960dd2a","added_by":"auto","created_at":"2026-05-18 16:55:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":560436,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedHybridQuantumpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8940353/v1_covered_a263ffe4-0d0d-4d94-ae0b-af6b2c81ac68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Quantum-Classical Neural Network for Automated Pneumonia Detection from Chest X-Rays: A Comparative Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Quantum Machine Learning, Pneumonia Detection, Hybrid Neural Networks, Variational Quantum Circuits, PennyLane, Medical Image Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8940353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8940353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePneumonia remains a leading cause of mortality globally, necessitating rapid and accurate diagnostic tools to assist radiologists. While classical Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), have demonstrated high efficacy in medical image analysis, they are often characterized by high computational complexity and massive parameter requirements. This research proposes a novel \u003cb\u003eHybrid Quantum-Classical Neural Network (HQCNN)\u003c/b\u003e to detect Pneumonia from chest X-ray images with enhanced computational efficiency. By integrating a classical ResNet-18 architecture for feature extraction with a \u003cb\u003eVariational Quantum Circuit (VQC)\u003c/b\u003e based on PennyLane, we leverage the quantum mechanical principles of superposition and entanglement for pattern recognition. The proposed model was trained and validated on a standard chest X-ray dataset and benchmarked against a classical transfer learning equivalent. Experimental results indicate that the hybrid quantum model achieved a validation accuracy of \u003cb\u003e78%\u003c/b\u003e with significantly fewer trainable parameters (~\u0026thinsp;24) compared to the classical baseline (62% accuracy, ~\u0026thinsp;1026 parameters). Furthermore, the quantum model demonstrated superior sensitivity (100% Recall), successfully identifying all positive pneumonia cases in the validation batch. This study highlights the potential of Quantum Machine Learning (QML) as a resource-efficient, high-sensitivity alternative for future medical diagnostic systems.\u003c/p\u003e","manuscriptTitle":"Hybrid Quantum-Classical Neural Network for Automated Pneumonia Detection from Chest X-Rays: A Comparative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 06:33:30","doi":"10.21203/rs.3.rs-8940353/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":"e457d1fc-8239-4235-9f08-4b17de5c6377","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T16:49:13+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T16:54:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 06:33:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8940353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8940353","identity":"rs-8940353","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}