A Comprehensive Review of Diagnostic Accuracy of Glaucoma Detection Methods Using Retinal Fundus Imaging

Abstract

This survey paper aims to provide an overview of the various techniques and methodologies used for glaucoma detection using fundus images of the retina. Glaucoma is a leading cause of irreversible blindness worldwide, and early detection is crucial for effective treatment. Fundus images, which capture the structure of the retina, have become a valuable tool for diagnosing glaucoma. This paper reviews the state-of-the-art techniques, including image processing, machine learning, and deep learning approaches, used for automated glaucoma detection.