Retinal Disease Classification

Retinal disease classification aims to automatically identify various eye diseases from retinal images, enabling earlier and more efficient diagnosis. Current research heavily utilizes deep learning, particularly transformer-based architectures and convolutional neural networks, often incorporating attention mechanisms to focus on relevant image features and address class imbalances inherent in medical datasets. These advanced models show improved accuracy compared to traditional methods, potentially assisting ophthalmologists in diagnosis and contributing to better patient outcomes by facilitating earlier intervention. The development of large, diverse datasets and the exploration of techniques like counterfactual image generation further enhance the robustness and explainability of these diagnostic tools.