Eye Disease

Eye disease research intensely focuses on developing automated diagnostic tools using deep learning, aiming to improve efficiency and accuracy in detecting and classifying various conditions like diabetic retinopathy and age-related macular degeneration. Current efforts leverage architectures such as ResNet, Inception, and transformer models, often incorporating techniques like contrastive learning, attention mechanisms, and generative flow networks to enhance performance and interpretability. These advancements hold significant promise for addressing the growing need for efficient and accessible eye disease diagnosis, particularly in resource-constrained settings, by reducing reliance on manual interpretation and improving diagnostic accuracy.