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Experiments on MESSIDOR and HRF show high sensitivity, specificity, and accuracy, with low segmentation time and validated generalization on additional 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reuse guidelines:  \ngrading system using deep learning  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20552076251410982](DOI: 10.1177/20552076251410982)  \napproach   \n[journals.sagepub.com/home/dhj](journals.sagepub.com/home/dhj)  \nD. Siva Sundhara Raja 1 , S. Kumarganesh2 , K. Martin Sagayam3  and Hien Dang4,5   \nAbstract  \nEarly screening for diabetic retinopathy (DR) prevents vision loss in diabetic patients.  \nObjective: To classify DR cases, this study suggests computer-assisted screening and diagnosis. The proposed methodology consists of retinal blood vessel, macular region, and exudate segmentation. In this study, exudates were screened, and DR was classified as mild, moderate, or severe by analyzing the presence of exudates in the macular region. Methods: The bit-plane morphological slicing technique was employed to segment the macular region. The U-Net deep learning approach was used to segment the exudates from the retinal images, and the proposed methodologies were applied and evaluated on retinal images in the publicly available Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (MESSIDOR) and High-Resolution Fundus (HRF) datasets. Finally, DR can be diagnosed by analyzing the presence of exudates in the macular region, and DR can be diagnosed as mild, moderate, or severe based on the severity levels. The conventional segmentation algorithms segment and locate the internal boundary of pixels in both exudates and macula. In contrast with the conventional segmentation methods, the proposed algorithm segments and locates both internal and external boundary of pixels in both exudates and macula region which increases the segmentation accuracy.  \nResults: The proposed macular region segmentation method obtained 98.9% SeI, 99.2% SpI, and 99.1% AccI for the MESSIDOR dataset. The proposed macular region segmentation method obtained 99.2% SeI, 99.7% SpI, and 99.8% AccI for the HRF dataset. The proposed exudate segmentation method obtained 99.3% SeI, 99.2% SpI, and 99.2% AccI for the MESSIDOR dataset. The proposed exudate segmentation method obtained 99.7% SeI, 99.3% SpI, and 99. 1% AccI for the HRF dataset.  \nConclusion: The segmentation time period of both exudates and macula region in retinal image is low when comparing with other segmentation methods. Moreover, this proposed segmentation and diagnosis method was tested with other DIARETDB1, Retinal Fundus Multi-disease Image, and Fundus Image Registration Dataset datasets to validate the effectiveness of the proposed method.  \nKeywords  \nDiabetic retinopathy, macula region, vision loss, exudates  \nReceived: 7 July 2025; accepted: 9 December 2025  \n1 Department of ECE, SACS MAVMM Engineering College, Madurai, India 2Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India  \n3Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India  \n4Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam  \n5Department of Mathematics and Computer Science, Molloy University, Rockville Centre, NY, USA  \nCorresponding author:  \nHien Dang, Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.  \nEmail: [hiendt@tlu.edu.vn](hiendt@tlu.edu.vn)  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution  \nNonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ([https://us](https://us). [sagepub.com/en-us/nam/open-access-at-sage](sagepub.com/en-us/nam/open-access-at-sage","cbCaipLR0QYEwIaJ","https://ap.wps.com/l/cbCaipLR0QYEwIaJ","pdf",2288330,18,"English","# Abstract\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the goal of the proposed DR system?\",\"answer\":\"To classify diabetic retinopathy cases and support computer-assisted screening and diagnosis by grading DR as mild, moderate, or severe.\"},{\"question\":\"How does the method determine DR severity?\",\"answer\":\"It analyzes the presence of exudates in the macular region, using macular-region segmentation and U-Net-based exudate segmentation.\"},{\"question\":\"Which datasets were used to evaluate the approach?\",\"answer\":\"The method was evaluated on MESSIDOR and High-Resolution Fundus (HRF), and further tested on DIARETDB1, Retinal Fundus Multi-disease Image, and Fundus Image Registration Dataset.\"}]","Diabetic retinopathy detection and grading system using deep learning approach | PDF",1790756671,45]