Deep Learning with Class Imbalance for Detecting and Classifying Diabetic Retinopathy on Fundus Retina Images

Deep Learning with Class Imbalance for Detecting and Classifying Diabetic Retinopathy on Fundus Retina 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 Deep Learning with Class Imbalance for Detecting and Classifying Diabetic Retinopathy on Fundus Retina Images Kamel Kamal, Rania. A. Mohamed, Ashraf Darwish, Aboul Ella Hassanien This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1935432/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 Diabetes mellitus is a disorder that causes diabetic retinopathy and is the primary cause of blindness worldwide. Early detection and treatment are required to reduce or avoid vision degradation and loss. For that purpose, various artificial-intelligence-powered approaches for detecting and classifying diabetic retinopathy on fundus retina images have been proposed by the scientific community. This article explores solutions to diabetic retinopathy detection by using three recently developed deep neural networks that have proven effective and efficient. Densenet201, Resnet101, and EfficientNetb0 deep neural network families have been applied to detect and classify diabetic retinopathy on fundus retina images. The dataset was notably not equilibrium; the widespread majority had been normal images, while mild Diabetic retinopathy images made up a very minor percentage of the total dataset. To treatment the skewed distribution and to keep away from biased classification results different scenarios have been used to balance the classes by utilizing (i) weight balancing with data augmentation; (ii) oversampling with data augmentation; (iii) focal loss with data augmentation, and (iv) a hybrid method of oversampling with a focal loss with data augmentation that improves the deep neural network performance of fundus retina images classification with the imbalanced dataset to build an expert system that can rapidly and adequately detect fundus images. The experimental results indicated that using Densenet201, Resnet101, and EfficientNetb0, with weight balancing on the dataset, substantially improves diabetic retinopathy prediction, by re-weighting each class in the loss function, a class that represents an under-represented class will receive a larger weight. The models yielded 94.74%, 94.74%, and 93.42%, respectively, on the test data set. Diabetic retinopathy deep learning imbalanced data set CNN architecture and Convolutional neural network 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. 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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-1935432","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":127725848,"identity":"b7dd3e66-b643-477b-95b8-6af57abf6b22","order_by":0,"name":"Kamel Kamal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYLCCDwwMCWAGD7E6GGeQrIWZhyQtBrePP/xsU1OXxz/7AOODt20McvIOhLScyzGWzjnGVixxLoHZcG4bg7HhAQJaJHt42Jhz2HgSG84wsEnztjEkbmwgqIX9GbPFP4nE+WcY2H8DtdQT1MLPw2DGzNhmkLgBaAszUEuCPAEdQC08xpK9fQmJG88wNkvOOSdhuIGQFjYe9ocffnyrS5x3hvnghzdlNvLyhByGBBhBaiUYDA4QrwUKSLFlFIyCUTAKRgYAAEwIN80j8ETBAAAAAElFTkSuQmCC","orcid":"","institution":"Al-Azhar University","correspondingAuthor":true,"prefix":"","firstName":"Kamel","middleName":"","lastName":"Kamal","suffix":""},{"id":127725849,"identity":"693b7ea7-fad8-4389-af09-13910edeeb88","order_by":1,"name":"Rania. 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Early detection and treatment are required to reduce or avoid vision degradation and loss. For that purpose, various artificial-intelligence-powered approaches for detecting and classifying diabetic retinopathy on fundus retina images have been proposed by the scientific community. This article explores solutions to diabetic retinopathy detection by using three recently developed deep neural networks that have proven effective and efficient. Densenet201, Resnet101, and EfficientNetb0 deep neural network families have been applied to detect and classify diabetic retinopathy on fundus retina images. The dataset was notably not equilibrium; the widespread majority had been normal images, while mild Diabetic retinopathy images made up a very minor percentage of the total dataset. To treatment the skewed distribution and to keep away from biased classification results different scenarios have been used to balance the classes by utilizing (i) weight balancing with data augmentation; (ii) oversampling with data augmentation; (iii) focal loss with data augmentation, and (iv) a hybrid method of oversampling with a focal loss with data augmentation that improves the deep neural network performance of fundus retina images classification with the imbalanced dataset to build an expert system that can rapidly and adequately detect fundus images. The experimental results indicated that using Densenet201, Resnet101, and EfficientNetb0, with weight balancing on the dataset, substantially improves diabetic retinopathy prediction, by re-weighting each class in the loss function, a class that represents an under-represented class will receive a larger weight. 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