Abstract:
Glaucoma, a leading cause of irreversible blindness worldwide, necessitates early and accurate diagnosis to prevent significant vision loss. Recent advancements in deep learning have revolutionized medical imaging, offering promising tools for automated glaucoma detection from fundus images. This review paper provides a comprehensive overview of the current state-of-the-art deep learning techniques for glaucoma identification, summarizing various methodologies, datasets, performance metrics, and highlighting the challenges and future directions in this domain.
Keywords: