Image-Based Monitoring for Diagnosing Retinopathy Using an Advanced Deep Learning Algorithm

Abstract

Retinopathy, particularly diabetic retinopathy, remains a leading cause of preventable blindness worldwide, underscoring the need for rapid, accurate, and scalable diagnostic solutions. This study presents an advanced deep learning algorithm designed for image- based monitoring and diagnosis of retinopathy. Leveraging a large dataset of retinal fundus images, the model employs a convolutional neural network (CNN) architecture optimized for feature extraction and classification of disease severity. The proposed system demonstrates high accuracy, sensitivity, and specificity in detecting early signs of retinopathy, outperforming traditional diagnostic methods. Additionally, the model incorporates explainability techniques, such as saliency mapping, to provide clinicians with visual insights into the decision-making process. Our results suggest that deep learning-driven image analysis can significantly enhance early detection, ongoing monitoring, and management of retinopathy, offering a promising tool for integration into routine clinical practice and teleophthalmology services.