A Deep Learning–Driven CNN Framework for Early Identification of Retinal Disorders - khan Mohamed, jalaldeen; Ramchitra, Y; Gayathri, C; Karthiyayini, G; Priya, S Shanmuga; Sundar, S Lakshmana Nainar (International Journal of Medical Toxicology and Forensic Medicine, 2026)
Background: Early detection of retinal disorders is crucial to prevent vision loss. Traditional methods are time-consuming and prone to error. This study investigates a CNN-based deep learning framework for automating retinal disorder identification. Methods: The study, conducted from January to April 2025 at the Department of Electronics and Communication Engineering, used publicly available retinal image datasets. The CNN model was trained on preprocessed retinal fundus images, with performance evaluated using accuracy, sensitivity, specificity, precision, recall, and F1-score. Results: The proposed CNN framework exhibited excellent classification performance, achieving an overall accuracy of 94.6%, sensitivity of 93.2%, and specificity of 95.8%. The model successfully identified early-stage retinal abnormalities and demonstrated consistent results across various image samples. These findings suggest that the deep learning approach significantly outperforms conventional methods in early retinal disorder detection. Conclusion: The CNN-based deep learning framework developed in this study provides a highly accurate and reliable tool for the early identification of retinal disorders. This approach has significant potential for integration into computer-aided diagnosis systems and tele-ophthalmology platforms, aiding early clinical decision-making and potentially preventing vision loss.