A Deep Learning Approach To Computer Aided Glaucoma Diagnosis

Abstract

Glaucoma has been listed as a major health deterrent and is one of the top three causes of vision loss which may lead to permanent blindness. Recent global health evaluation on primary health challenges conducted by World Health Organization (WHO) has identified eye related defects as one of the critical few. Survey reports highlight that if not treated, this can become a primary concern by 2020 leading to around 80 million people affected due to eye related defects. Irrespective of geologically being developed or developing country, retinal eye defects have progressing significantly over the earlier part of this century. Progression of eye defects can be reduced by the timely diagnosis of eye defects. Image processing in the recent years has gained traction and growth multiple avenues from facial recognition to computer aided diagnosis of diseases. Cost effective and efficient computer aided diagnosis of fundal abnormalities have been enabled using image processing. This paper discusses the different methodologies adopted for automatic detection and gives insight into the progression of image mining techniques.