Visual Impairment Prevention by Early Detection of Diabetic Retinopathy Based on Stacked Auto- Encoder

Visual Impairment Prevention by Early Detection of Diabetic Retinopathy Based on Stacked Auto- Encoder | 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 Visual Impairment Prevention by Early Detection of Diabetic Retinopathy Based on Stacked Auto- Encoder Shagufta Almas, Fazli Wahid, Sikandar Ali, Ahmed Alkhyyat, Kamran Ullah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5133023/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been developed to assist in DR detection, there remains a need for accurate and efficient methods to classify its stages. In this study, we propose a novel approach utilizing enhanced stacked auto-encoders for the detection and classification of DR stages. The stages are classified into five categories: normal, mild, moderate, severe, and proliferative. Unlike traditional CNN approaches, our method offers improved reliability by reducing time complexity, minimizing errors, and enhancing noise reduction. Leveraging a comprehensive dataset from KAGGLE containing 35,126 retinal fundus images across five DR stages, our proposed model demonstrates superior accuracy compared to existing deep learning algorithms. Data augmentation techniques address class imbalance, while SAEs facilitate accurate classification through layer-wise unsupervised pre-training and supervised fine-tuning. We evaluate our model's performance using rigorous quantitative measures, including accuracy, recall, precision, and F1-score, highlighting its effectiveness in early disease diagnosis and prevention of blindness. Experimental results across different training/testing ratios (50:50, 60:40, 70:30, and 75:25) showcase the model's robustness. The highest accuracy achieved during training was 93%, while testing accuracy reached 88% on a training/testing ratio of 75:25. Comparative analysis underscores the model's superiority over existing methods, positioning it as a promising tool for early-stage DR detection and blindness prevention. diabetic retinopathy disability deep learning stacked auto-encoder dropout Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Nov, 2024 Reviews received at journal 04 Nov, 2024 Reviews received at journal 22 Oct, 2024 Reviews received at journal 18 Oct, 2024 Reviewers agreed at journal 13 Oct, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers invited by journal 08 Oct, 2024 Editor assigned by journal 08 Oct, 2024 Editor invited by journal 08 Oct, 2024 Submission checks completed at journal 07 Oct, 2024 First submitted to journal 22 Sep, 2024 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. 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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-5133023","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375547354,"identity":"2d11ab76-cae2-4a39-bb6c-f72ffb71f254","order_by":0,"name":"Shagufta Almas","email":"","orcid":"","institution":"The University of Haripur","correspondingAuthor":false,"prefix":"","firstName":"Shagufta","middleName":"","lastName":"Almas","suffix":""},{"id":375547355,"identity":"6220d192-f160-4b57-8c59-b7cfef11ae5a","order_by":1,"name":"Fazli Wahid","email":"","orcid":"","institution":"The University of 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