Approaches for early detection of glaucoma using retinal images: A performance analysis

Abstract

Sight is one of the most important senses for humans, as it allows them to see and explore their surroundings. Multiple ocular diseases damaging sight have been detected over the years such as glaucoma and diabetic retinopathy. Glaucoma is a group of diseases that can lead to blindness if left untreated. No cure for glaucoma exists apart from early detection and treatment by an ophthalmologist. Retinal images provide vital information about an eye’s health. On the basis of advancements in retinal images technology it is possible to develop systems that can analyze these images for better diagnosis. To test the efficiency of some of the developed techniques, we obtained the code for four different approaches and did a performance analysis using four public datasets. We investigated the results along with the analysis time. The outcomes of the study are approaches for glaucoma detection;behavior of glaucoma related approaches on retinal images with different ocular diseases;challenges faced when analyzing retinal images; andglaucoma risk factors.