Detection and Grading of Diabetic Retinopathy using Optimized BiLSTM Classifier

Detection and Grading of Diabetic Retinopathy using Optimized BiLSTM Classifier | 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 Detection and Grading of Diabetic Retinopathy using Optimized BiLSTM Classifier Archana Senapati, Hrudaya Kumar Tripathy, Sushruta Mishra, Saurav Mallik, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4551982/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 Diabetic retinopathy (DR) is a common consequence of diabetes mellitus resulting in vision-impairing lesions on the retina. Treatment of DR in its early stages can extensively minimize the chance of blindness. Diverse machine learning approaches were developed for DR detection; however, the classical models may create certain limitations including overfitting issues, data requirements, and vanishing gradient problems. To mitigate these shortcomings, this research proposed a Wolf social leader algorithm-enabled Bi-directional long short-term memory (WS-BiLSTM) for DR detection. The integration of a weighted shape-based texture pattern enhances the capability of the model to extract pertinent texture and shape features. Additionally, the ResNet 101 model obtains the informative regions from the fundus images which leads to attaining better performance. The statistical features extracted from the input fundus images enhance the robustness of the framework. The hyperparameters of the WS-BiLSTM model are optimized using the suggested Wolf social leader algorithm, which imitates the social dynamics of American jackals and the hunting characteristics of gray wolves. In addition, the model improves the performance effectively with high detection performance and achieved accuracy, sensitivity, and specificity of 96.32%, 97.21%, and 95.42% compared to other convolutional methods. Health sciences/Diseases/Eye diseases Physical sciences/Engineering Physical sciences/Mathematics and computing Diabetic retinopathy detection and grading Wolf social leader algorithm Bi-directional long short-term memory weighted shape-based texture pattern ResNet 101 feature flow 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-4551982","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318407791,"identity":"7f683094-697d-4236-83e2-52817b6680f6","order_by":0,"name":"Archana Senapati","email":"","orcid":"","institution":"Kalinga Institute of Industrial Technology, Deemed to be University","correspondingAuthor":false,"prefix":"","firstName":"Archana","middleName":"","lastName":"Senapati","suffix":""},{"id":318407792,"identity":"8732ca51-3ba9-415a-b28e-63f48a0192d1","order_by":1,"name":"Hrudaya Kumar Tripathy","email":"","orcid":"","institution":"Kalinga Institute of Industrial Technology, Deemed to be University","correspondingAuthor":false,"prefix":"","firstName":"Hrudaya","middleName":"Kumar","lastName":"Tripathy","suffix":""},{"id":318407793,"identity":"4636fbda-604d-4107-8a08-6ebde6a3f8ea","order_by":2,"name":"Sushruta Mishra","email":"","orcid":"","institution":"Kalinga Institute of Industrial Technology, Deemed to be University","correspondingAuthor":false,"prefix":"","firstName":"Sushruta","middleName":"","lastName":"Mishra","suffix":""},{"id":318407794,"identity":"797c7ab4-8f5c-4bd1-b8b6-b44aa935a810","order_by":3,"name":"Saurav Mallik","email":"","orcid":"","institution":"Harvard T. 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Treatment of DR in its early stages can extensively minimize the chance of blindness. Diverse machine learning approaches were developed for DR detection; however, the classical models may create certain limitations including overfitting issues, data requirements, and vanishing gradient problems. To mitigate these shortcomings, this research proposed a Wolf social leader algorithm-enabled Bi-directional long short-term memory (WS-BiLSTM) for DR detection. The integration of a weighted shape-based texture pattern enhances the capability of the model to extract pertinent texture and shape features. Additionally, the ResNet 101 model obtains the informative regions from the fundus images which leads to attaining better performance. The statistical features extracted from the input fundus images enhance the robustness of the framework. 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