Deep Learning Framework for Multiclass Detection of Ocular Diseases in Fundus Images

Deep Learning Framework for Multiclass Detection of Ocular Diseases in Fundus 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 Article Deep Learning Framework for Multiclass Detection of Ocular Diseases in Fundus Images Shajila Beegam, Mala Kalra, Abhijit Bhowmik, Jibitesh Kumar Panda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6920228/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 The early and accurate detection of eye diseases is paramount in preventing irreversible vision loss and facilitating timely treatment. Conventional diagnostic strategies generally depend on subjective analysis of experts leading to variability in diagnosis. Convolutional Neural Networks (CNNs) have evolved as a prospective solution to classify diseases using medical images with remarkable accuracy. This study proposes a CNN-based methodology for the multiclass classification of ocular diseases, including diabetic retinopathy (DR), cataract, and glaucoma. The objective is to improve the detection and evaluation of these conditions, thereby enabling effective intervention and patient management. The research introduces a 9-layer CNN designed for the automated classification of eye disorders, utilizing two datasets of fundus images. The CNN proficiently distinguishes between normal and disease-related features. To enhance the model's performance, preprocessing techniques and hyperparameter optimization are applied. The model is implemented using TensorFlow and Python within a Jupyter Notebook environment. With a learning rate (LR) set at 0.0001 and a batch size (BS) of 8, the proposed CNN achieves a training accuracy of 99.94% and a testing accuracy of 89.82% on the first dataset. When the batch size is increased to 32 while keeping the learning rate at 0.0001, the CNN model attains a training accuracy of 99.97% and a testing accuracy of 96.15% on the second dataset. The results indicate that this deep learning (DL) model demonstrates outstanding performance in classifying DR, cataract, glaucoma, and healthy eye conditions from fundus images, and the proposed approach can assist ophthalmologists in accurately diagnosing eye diseases. Health sciences/Health care Physical sciences/Engineering Fundus Images Convolutional Neural Network Multiclass Eye Disease Detection Artificial Intelligence Deep Learning 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-6920228","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477788686,"identity":"523fe475-7533-46db-b4b1-3214895ee18b","order_by":0,"name":"Shajila Beegam","email":"","orcid":"","institution":"NITTTR","correspondingAuthor":false,"prefix":"","firstName":"Shajila","middleName":"","lastName":"Beegam","suffix":""},{"id":477788687,"identity":"326b7438-74fd-45c6-b914-99e89371433c","order_by":1,"name":"Mala Kalra","email":"","orcid":"","institution":"NITTTR","correspondingAuthor":false,"prefix":"","firstName":"Mala","middleName":"","lastName":"Kalra","suffix":""},{"id":477788694,"identity":"ef977297-7bdc-45ed-96c5-24811915e645","order_by":2,"name":"Abhijit Bhowmik","email":"","orcid":"","institution":"SIMATS, Saveetha Institute of Medical and Technical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abhijit","middleName":"","lastName":"Bhowmik","suffix":""},{"id":477788697,"identity":"b30be298-7cbf-4d01-b7a4-13d35f098d31","order_by":3,"name":"Jibitesh Kumar Panda","email":"data:image/png;base64,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","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Jibitesh","middleName":"Kumar","lastName":"Panda","suffix":""}],"badges":[],"createdAt":"2025-06-18 07:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6920228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6920228/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91346945,"identity":"466e7d7c-9c12-4607-a9d9-8241774b252d","added_by":"auto","created_at":"2025-09-15 14:02:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":832663,"visible":true,"origin":"","legend":"","description":"","filename":"Dr.JibiteshFiguresSpecified.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6920228/v1_covered_305d742c-3ecb-4ba9-a3b6-7fb1808511d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning Framework for Multiclass Detection of Ocular Diseases in Fundus Images","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":"Fundus Images, Convolutional Neural Network, Multiclass Eye Disease Detection, Artificial Intelligence, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-6920228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6920228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe early and accurate detection of eye diseases is paramount in preventing irreversible vision loss and facilitating timely treatment. Conventional diagnostic strategies generally depend on subjective analysis of experts leading to variability in diagnosis. Convolutional Neural Networks (CNNs) have evolved as a prospective solution to classify diseases using medical images with remarkable accuracy. This study proposes a CNN-based methodology for the multiclass classification of ocular diseases, including diabetic retinopathy (DR), cataract, and glaucoma. The objective is to improve the detection and evaluation of these conditions, thereby enabling effective intervention and patient management. The research introduces a 9-layer CNN designed for the automated classification of eye disorders, utilizing two datasets of fundus images. The CNN proficiently distinguishes between normal and disease-related features. To enhance the model's performance, preprocessing techniques and hyperparameter optimization are applied. The model is implemented using TensorFlow and Python within a Jupyter Notebook environment. 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