CDR-LWP: Layer-Wise Probability Fusion and Interpretable Deep Learning for Multi-Stage Diabetic Retinopathy Classification

CDR-LWP: Layer-Wise Probability Fusion and Interpretable Deep Learning for Multi-Stage Diabetic Retinopathy Classification | 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 CDR-LWP: Layer-Wise Probability Fusion and Interpretable Deep Learning for Multi-Stage Diabetic Retinopathy Classification Saif Khalid Musluh, Hatem A. Rashwan, Ammar M. Okran, Sylvie Chambon, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7543729/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 is one of the leading causes of visual impairment and blindness among individuals with diabetes. This em- phasizes the need for accurate and early classification to enable timely intervention. This study proposes a novel deep learning framework based on VGG16 for classifying DR into five severity levels. In contrast to con- ventional approaches that rely exclusively on final-layer outputs, the pro- posed model leverages features extracted from all convolutional layers, thus capturing both low- and high-level visual representations. These multi-scale features are processed through fully connected layers to esti- mate layer-wise probability distributions, which are then aggregated us- ing a weighted network to perform the final classification. To enhance fea- ture refinement and discriminative capability, a Fusion Refinement Block (FRB) is incorporated to improve multi-scale feature fusion, while a Spa- tial Attention (SA) mechanism is employed to focus on the most relevant retinal regions. Furthermore, oversampling is used to address class imbal- ance, and contrast-limited adaptive histogram equalization (CLAHE) is applied to improve the visibility of blood vessels in fundus images. The proposed model is evaluated on multiple benchmark datasets (IDRiD, APTOS, DDR, and EyePACS), achieving classification precision ranging from 0.8397% to 0.9372% and quadratic weighted kappa scores ranging from 0.8218% to 0.9623% with the three datasets, thus demonstrating its effectiveness and robustness in DR classification tasks. This project code is available at https://github.com/saifalkhaldiurv/CDR-LWP.git . Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Diabetic Retinopathy Deep Learning Fundus Imaging Feature Extraction Multi-scale Fusion Spatial Attention Block (SA) (SA) Fusion Refinement Block (FRB) Medical Image Classification 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-7543729","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":569320493,"identity":"b83f630f-ca66-4ce6-a215-101ac15c8a0b","order_by":0,"name":"Saif Khalid Musluh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3RsYrCMBzH8V8I9JZeXXWxr9AiOCm+SorDPYAgt11c4thVQd9BF+cefziX4pzjHOxyk0Ie4dIeDnIQHW/Ilwyh8CH5N4DP9y9j0qAoEHGkp+ZD0SwnmS1qEnD0kgcJ2C8B+u0rcRbPaSZRHuPgie9eLwrdSAtWnB0kKTNL9HeqeDD9XCv0OlrgfeUiyOYnGGKKh/2vZ4VsYwmFrovllT3F0KgmE0ve7hLo5mKU1YRbIpJ7JNH2FFHS2M4y6awO7XRZVtI5S5y/VNJ80DBv0dacp4M42o/JuP5Yk7huQtRPwyScs9wU/tn4fD6fD/gBAUVXNVzyibcAAAAASUVORK5CYII=","orcid":"","institution":"Universitat Rovira i Virgili","correspondingAuthor":true,"prefix":"","firstName":"Saif","middleName":"Khalid","lastName":"Musluh","suffix":""},{"id":569320494,"identity":"c7a333cb-87e7-4ea4-bfc8-2f00b054855d","order_by":1,"name":"Hatem A. 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