Diabetic Retinopathy

Diabetic retinopathy (DR), a leading cause of blindness in diabetics, necessitates early and accurate detection for effective treatment. Current research heavily focuses on developing and improving automated DR detection and grading systems using deep learning, employing various convolutional neural network (CNN) architectures (e.g., ResNet, Inception, VGG) and incorporating techniques like transfer learning, self-supervised learning, and multimodal fusion of retinal images (e.g., combining color fundus photography and optical coherence tomography angiography). These advancements aim to improve diagnostic accuracy, efficiency, and generalizability across diverse datasets and clinical settings, ultimately impacting patient care and reducing the global burden of vision loss from DR.