Abstract: Diabetic Retinopathy (DR) represents a progressive ocular pathology characterized by retinal deterioration resulting from sustained hyperglycemia in diabetic patients. This microvascular complication constitutes the predominant etiology of visual impairment among working-age populations in developing nations. Given the irreversible nature of vision loss associated with advanced DR, therapeutic interventions primarily focus on preserving residual visual function through early detection and timely management. The current diagnostic paradigm relies heavily on manual interpretation of retinal fundus photography by ophthalmological specialists, creating significant challenges in terms of time consumption, economic burden, and resource allocation. These limitations are particularly pronounced during initial disease stages when pathological manifestations may be subtle and difficult to identify through conventional screening methods. Contemporary artificial intelligence approaches, specifically deep learning algorithms, offer promising solutions for automated analysis of retinal imagery, facilitating earlier diagnosis and more efficient screening protocols. This comprehensive review examines various automated methodologies developed for detecting DR and classifying its severity, providing a detailed analysis of their performance characteristics, dataset utilization, and clinical applicability. The investigation encompasses multiple deep learning architectures, their comparative advantages, and the potential for integrating them into existing healthcare delivery systems.
Keywords: Artificial Intelligence, Convolutional Neural Networks, Medical Image Analysis, Retinal Pathology, Computer-Aided Diagnosis
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DOI:
10.17148/IJARCCE.2025.14691
