Advancing Eye Disease and Glaucoma Diagnosis: A Comprehensive Survey of Deep Learning, Machine Learning, and Hybrid Approaches

Glaucoma stands as a serious global health issue among eye diseases because the condition leads to permanent blindness when proper diagnosis and treatment are not made in sufficient time. The detection and classification capabilities for these conditions have been significantly improved by recent developments in computational techniques that combine deep learning and traditional machine learning and hybrid models alongside clinical assessments. The survey explores 54 research studies regarding eye disease diagnosis which the authors divide into four distinct sections. Deep learning algorithms particularly CNN variants achieved outstanding success (93.5%) when used for retinal image segmentation together with disease classification tasks. Hybrid models equipped with machine learning and deep learning capabilities reached the maximum accuracy level at 95% for diagnosing diseases. SVMs and other traditional machine learning algorithms delivered accurate results for structured datasets through their performance while medical evaluations helped identify disease trajectories and disease elements. The study presents a thorough overview of the advantages along with drawbacks and potential applications for these analysis techniques in detecting and treating glaucoma along with other eye conditions.