Deep Learning for the Detection and Classification of Diabetic Retinopathy Stages

The incidence of diabetic retinopathy (DR), a complication of diabetes leading to severe vision impairment and potential blindness, has surged worldwide in recent years. This condition is considered one of the leading causes of vision loss. To improve diagnostic accuracy for DR and reduce the burden on healthcare professionals, artificial intelligence (AI) methods are increasingly implemented in medical institutions. AI-based models, in particular, are integrating more algorithms to enhance the performance of existing neural network architectures that are commercially used for DR detection. However, these neural network models still exhibit limitations, such as the need for high computational power and lower accuracy in detecting early DR stages. To overcome these challenges, developing more advanced machine learning models for precise DR detection and classification of DR stages is essential, as it would aid ophthalmologists in making accurate diagnoses. This article reviews current research on the use of deep learning in diagnosing and classifying DR and related diseases, as well as the challenges ophthalmologists face in detecting this condition and potential solutions for early-stage DR detection. This review provides information on modern approaches to DR detection using deep learning applications and discusses the issues and limitations in this area.