Retinal Disease Classification Using Deep Learning

Abstract: Retinal eye diseases pose significant challenges in accurate diagnosis and timely intervention. The complexities arising from diverse pathological variations within retinal images, coupled with the scarcity and imbalance inherent in medical datasets, underscore the critical need for advanced methodologies. This research addresses these challenges by proposing a novel framework that integrates (CNNs) and (GANs) for improved retinal disease classification. The utilization of CNNs aims to effectively extract features from intricate retinal structures, while GANs play a pivotal role in addressing dataset limitations through synthetic image generation. The proposed solution strives to overcome these obstacles,offering a promising avenue for enhanced diagnostic accuracy in retinal eye diseases.