A Critical Review on Ophthalmic Diagnosis of Glaucoma in Fundus Images of Eye using Deep Learning Models

A Critical Review on Ophthalmic Diagnosis of Glaucoma in Fundus Images of Eye using Deep Learning Models
Author(s):
Santhosh S, Dr. D. Veerabhadra Babu
Keywords:
Fundus Images, Glaucoma, CAD, Deep learning technique.
Abstract
A condition known as glaucoma, which affects the optic nerve, can cause vision loss that is either partial or total. It happens as a result of abnormal intraocular pressure within the eye, which damages the optic nerve. Since glaucoma does not have any symptoms when it is first diagnosed, it is crucial to stop blindness from occurring. Therefore, there is a critical need for glaucoma screening at an early age. Ophthalmologists prefer fundus photography, which is both convenient and affordable, to aid in the diagnosis of glaucoma. The use of CAD (computer Aided Detection) is particularly helpful in the diagnosis of glaucoma and can greatly lessen the clinicians' effort. We've also talked about the benefits of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis. This survey examines different cutting-edge CAD tools and techniques for the precise identification of glaucoma utilizing deep learning methodology.
Article Details
Unique Paper ID: 157443 Publication Volume & Issue: Volume 9, Issue 7 Page(s): 261 - 264
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