Quantum-Enhanced CNN for Multi-Class Retinal Disease Classification from OCT Images

Quantum-Enhanced CNN for Multi-Class Retinal Disease Classification from OCT Images | 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 Quantum-Enhanced CNN for Multi-Class Retinal Disease Classification from OCT Images lovish bhatia, Shreyansh Gupta, Rizil Patel, Vinay Shankar Pandey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9170670/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 This study demonstrates that a hybrid CNN–QNN architecture outperforms classical CNNs for multi-class retinal OCT classification by leveraging variational quantum circuits to enhance feature separability. Early detection of retinal diseases through optical coherence tomography (OCT) imaging is critical for timely intervention and vision preservation. While classical convolutional neural networks (CNNs) excel at pattern recognition in retinal OCT B-scans, their ability to capture complex, non-linear feature relationships in subtle disease manifestations remains limited. Hybrid quantum-classical architectures offer a promising approach by leveraging variational quantum circuits to enhance feature separability in challenging classification regimes. This study presents a hybrid CNN–QNN architecture for multi-class classification of retinal OCT images dataset (CNV, DME, DRUSEN, NORMAL). A classical CNN backbone extracts hierarchical spatial features from B-scans, which are then processed through parallel classical and quantum classification heads. The quantum head employs a 5-qubit variational quantum circuit with angle embedding and strongly entangling layers to generate quantum-enhanced feature representations. Outputs from both heads are concatenated and passed through a final softmax layer for four-class prediction. The findings establish hybrid quantum-classical neural networks as a viable enhancement to conventional deep learning for retinal OCT analysis, offering improved discriminative performance on established medical imaging benchmarks through quantum feature transformations. Artificial Intelligence and Machine Learning Quantum Machine Learning retinal OCT disease deep learning Hybrid quantum model Full Text Additional Declarations The authors declare no competing interests. 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-9170670","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608965433,"identity":"f66c7ff8-f956-4cb2-bae7-2c7145070089","order_by":0,"name":"lovish bhatia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACPiA+wNhwgJmNvQHINLAgrIUNqoWdj+cASIsEcVoYgFr45SQSQExitLD3GB78ueOONJvk86sbfhRIMPC3dyfg18JzxuAw75lnxmzSOWU3e4AOkzhzdgN+LRJpCYcZ2w4nA7Wk3eABajGQyCWs5eDPtsP1bZJn0m7+IU5L8oEDvG2Hmdkk2I/dJs4WnsMHDvO2PWNm48lhuy1jIMFD0C/87I3NH3+23WGWbz/+7OabPzZy/O29+LUgAR4DMEmschBgf0CK6lEwCkbBKBhBAAAml0cW3misegAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0006-3928-2131","institution":"National Institute of Technology Delhi","correspondingAuthor":true,"prefix":"","firstName":"lovish","middleName":"","lastName":"bhatia","suffix":""},{"id":608965434,"identity":"513bac9b-3c94-441a-a1bb-998be5e8b3b5","order_by":1,"name":"Shreyansh Gupta","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Shreyansh","middleName":"","lastName":"Gupta","suffix":""},{"id":608965435,"identity":"ee024761-05e4-4d21-b391-a99da91cfdf8","order_by":2,"name":"Rizil Patel","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Rizil","middleName":"","lastName":"Patel","suffix":""},{"id":608965436,"identity":"32d41875-8e48-4d79-917e-a6076254758a","order_by":3,"name":"Vinay Shankar Pandey","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"Shankar","lastName":"Pandey","suffix":""}],"badges":[],"createdAt":"2026-03-19 14:37:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9170670/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9170670/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105562750,"identity":"d4f7a831-ec44-45d0-9d9f-3f7964928686","added_by":"auto","created_at":"2026-03-27 12:44:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195894,"visible":true,"origin":"","legend":"","description":"","filename":"QuantumEnhancedCNNforMultiClassRetinalDiseaseClassificationfromOCTImages.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9170670/v1_covered_04daf4a7-3ab3-4333-9166-36246cc10214.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eQuantum-Enhanced CNN for Multi-Class Retinal Disease Classification from OCT Images\u003c/p\u003e","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, retinal OCT disease, deep learning, Hybrid quantum model","lastPublishedDoi":"10.21203/rs.3.rs-9170670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9170670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study demonstrates that a hybrid CNN–QNN architecture outperforms classical CNNs for multi-class retinal OCT classification by leveraging variational quantum circuits to enhance feature separability. 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