A Survey of Glaucoma Detection Algorithms using Fundus and OCT Images

Glaucoma steals vision silently, but recent artificial intelligence advancements allow for its detection at an earlier stage with higher accuracy. This research updates the status of automated glaucoma detection by providing a literature review of recent technical articles since 2021 that use machine learning techniques. Brief descriptions of the various image types frequently present in glaucoma datasets are provided. At last, the papers have been summed up and sorted according to their most salient findings and the materials they used. Preprocessing methods for deep learning, transfer learning, ensemble approaches, self-organizing models, and hybrid features are among the most significant advances made with fundus images. Use of a public data set, OCT images in the spectral domain, and other novel methodologies are among the most significant advances made with OCT images.