Sensing Diabetic Retinopathy Using Deep Learning

Diabetic Retinopathy (DR) is a serious vision-threatening complication of diabetes, contributing significantly to global blindness rates. Early detection and accurate classification of DR are essential for effective treatment and the prevention of vision loss. Traditional diagnostic methods such as manual inspection of retinal images are time-consuming, labor-intensive, and subject to variability in expert opinion. Recent advancements in artificial intelligence and deep learning have provided promising solutions for automating this process. The proposed framework introduces an automated system for detecting and classifying diabetic retinopathy using Generative Adversarial Networks (GANs) integrated with Convolutional Neural Networks (CNNs). The system comprises three major stages: pre-processing, feature extraction, and classification. GANs are utilized for generating high-quality synthetic retinal images to enhance the dataset and improve model robustness. CNNs are employed to extract deep features and classify the severity of DR. This method significantly improves detection accuracy and generalization. Future developments will focus on increasing dataset diversity, optimizing the GAN architecture, and integrating the system for real-time screening applications in clinical settings.