Comprehensive Analysis of Glaucoma Detection using Machine Learning and Deep Learning

Glaucoma is an insidious and progressive eye condition that finally end up causing blindness in the patient. Traditional methods of screening are conspicuous for their dependence on the judgment of a clinician, which possibly miss the finer early signs. With the evolution of AI, specifically deep learning, researchers now utilize retinal fundus and OCT images to construct more objective and accurate diagnostic apparatuses. This review seeks to introduce some of the recent approaches involving machine learning, deep neural network architectures like U-Net, ResNet, and DenseNet, and Explainable AI tools such as SHAP and Grad-CAM, to highlight the affected regions of the eye so that the decision-making of the AI becomes more explainable and trustworthy. Although various papers are demonstrating high accuracy and promising results, problems remain in terms of limited datasets, variability of image quality, and computational intensity. Nevertheless, these AI-based methods provide a glimmer of hope for the future of the faster, earlier, and more accessible detection of glaucoma, particularly in deprived areas where routine eye care accessible.