Deep Learning-Based Automated Detection of Diabetic Retinopathy: A Review of Recent Developments

Diabetes mellitus can cause diabetic retinopathy (DR), a fatal ailment of the eyes that, may lead to blindness and optical loss if untreated. The increased occurrence of diabetes has made early detection of DR a vital public health issue. This study explores the topic of eye disorders, with a focus on DR because of its important influence on visual health. It examines the body of research on DR, emphasizing the signs and symptoms, course, and necessity of prompt treatment. The study also examines different approaches to deep learning detection, with a particular emphasis on their use in deep learning systems. In medical imaging and diagnostics, deep learning—a subset of artificial intelligence—has demonstrated amazing promise. Deep learning models are able to recognize patterns and anomalies by utilizing big datasets and sophisticated algorithms. The research compares the efficacy and performance of several deep learning models in identifying DR at various phases by presenting the findings of those models. The study ends with suggestions and future approaches, highlighting the necessity of ongoing study and advancement in deep learning methods for DR detection. It also emphasizes how crucial it is to incorporate these advanced methods into clinical practice in order to improve patient outcomes, increase early identification, and eventually lessen the impact of diabetic retinopathy on public health.