European Journal of Molecular & Clinical Medicine

Glaucoma is a category of eye disorders that are important for good vision and damage the optic nervous. The abnormally high pressure in your eye is often responsible for its damage. Glaucoma is one of the major blindness causes for people aged 60 or over. The glaucoma detection using Stochastic Gradient Descent (SGD) algorithm is described in this study. The input retinal images are given to frequency domain for feature extraction and SGD algorithm is used for detection. Experimental results show the performance of proposed system.

Blindness is a growing problem worldwide. The major causes of blindness are glaucoma and diabetic retinopathy. Increased intraocular pressure causes glaucoma. In glaucoma detection, it is very difficult to identify the edge of the optic cup because the image is blurred where blood vessels pass through the optic cup. Current methods do not effectively address the issue of peripheral blurring of the blood vessels surrounding the optic cup. In this paper, it is recommended to automatically detect glaucoma in retinal images using an efficient method. Initially, optic disk and cup segmentation is done by the Convolution Neural Network (CNN) and Modified Region Growing Mechanism (MRG). Then, texture features are extracted from the separated results. Finally, a neural network is used to diagnose glaucoma. The experimental results demonstrate that the proposed approach achieves better glaucoma detection result (accuracy, sensitivity and specificity) compared to few other approaches.