Glaucoma Diagnosis

Glaucoma diagnosis research focuses on developing accurate and efficient automated methods for detecting and grading this blinding eye disease, primarily using fundus photography and optical coherence tomography (OCT) images. Current efforts leverage deep learning, employing architectures like convolutional neural networks (CNNs), vision transformers, recurrent neural networks (RNNs), and capsule networks, often incorporating attention mechanisms and multi-modal data fusion to improve diagnostic accuracy and address challenges like domain shifts and data imbalance. These advancements aim to improve early detection, enabling timely intervention and potentially reducing the global burden of glaucoma by assisting ophthalmologists and expanding access to screening.