A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 14 | Issue : 03 | Page : 1 10
    By

    Umesh Kumar,

  1. Assistant Professor, Computer Engineering Department, JC Bose University of Science and Technology, YMCA, Faridabad, Haryana, Haryana, India

Abstract

Diabetic retinopathy (DR) is one of the main causes of vision impairment. Blindness prevention and effective treatment depend on early detection. A thorough deep learning-based framework for the automatic segmentation and simultaneous detection of exudates, hemorrhages, and microaneurysms – three important DR indicators – from retinal fundus images is presented in this work. These three pathological signs’ corresponding annotated image patches, along with background (no-sign) areas, were used to train a ten-layer convolutional neural network (CNN). A post-processing algorithm is used to improve localization and remove noise from the probability maps that the network produces for each class. Receiver operating characteristic (ROC) curve analysis was utilized to determine the optimal classification thresholds. The proposed model was evaluated on two publicly available datasets, one for training and another for testing. It uses both patch-based and full-image analysis. To ensure reliability, the model’s performance was tested across ten independent runs, consistently demonstrating high accuracy and reliable detection of all DR indicators. Furthermore, the approach was compared against several advanced models and techniques, including the tandem pulse coupled neural network (TPCNN), deep learning-based support vector machine (DLBSVM), synergic deep learning (SDL), and lesion-aware CNN (LACNN), using datasets, such as Messidor, DRIVE, CHASE_DB1, and OCT image databases. The proposed system exhibited competitive or superior results in terms of accuracy, specificity, and sensitivity. It confirms its potential as an effective and dependable tool for ophthalmologists in clinical DR screening.

Keywords: Diabetic retinopathy, fundus images, CNN, DR

[This article belongs to Research and Reviews : Journal of Computational Biology ]

How to cite this article:
Umesh Kumar. A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets. Research and Reviews : Journal of Computational Biology. 2025; 14(03):1-10.
How to cite this URL:
Umesh Kumar. A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets. Research and Reviews : Journal of Computational Biology. 2025; 14(03):1-10. Available from: https://journals.stmjournals.com/rrjocb/article=2025/view=223976


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Regular Issue Subscription Original Research
Volume 14
Issue 03
Received 18/05/2025
Accepted 14/07/2025
Published 19/08/2025
Publication Time 93 Days
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