Retinal Fundus Glaucoma Challenge

The Retinal Fundus Glaucoma Challenge focuses on developing automated methods for glaucoma detection and grading using retinal fundus images and optical coherence tomography (OCT) scans. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), vision transformers, and capsule networks, often incorporating attention mechanisms to enhance feature extraction and improve diagnostic accuracy. These efforts aim to improve early glaucoma detection, potentially leading to earlier interventions and better patient outcomes by leveraging the power of artificial intelligence to analyze complex medical images. The ultimate goal is to create robust and reliable AI-based tools that can assist ophthalmologists in diagnosis and screening, increasing accessibility and efficiency of glaucoma care.