Methods for Automatic Glaucoma Detection and Feature Extraction by Different Techniques at the Pre-processing Stage in Fundus Images

Glaucoma is a neurodegenerative, progressive and silent disease that affects the optic nerve, characterized by an increase in intraocular pressure causing irreversible damage to the optic nerve. The difficulty in early diagnosis of glaucoma has posed challenges at the technological and medical level, since it requires not only several years of study, but also experience on the part of medical specialists to examine the images and make a timely diagnosis. During this pandemic of COVID-19 many patients with this pathology have suffered constant changes and intraocular pressure has increased considerably, so early detection is of vital importance, in addition to providing appropriate and timely treatment so that the patient does not lose vision in its entirety and mitigate the effects that COVID-19 has caused in these patients. In this article we present the different techniques for the diagnosis of glaucoma and the automatic detection methods, as well as the analysis of image processing and the results obtained in the preprocessing stage, the characterization of this disease according to different points of view, we also present a thorough analysis of each of the methods proposed for the support of medical diagnosis, the characteristics of each of the classifiers and data of great relevance for future work.