Abstract

Glaucoma is the diagnosis given to a group of ocular conditions that contribute to the loss of retinal nerve fibers with a corresponding loss of vision.Glaucoma is the major cause of blindness in people above the age of 40. The Intra Ocular Pressure (IOP) increases because of the malfunction of the drainage structure of the eyes leading to Glaucoma. There are several methods to detect Glaucoma from a human eye in the initial stages. The proposed work automatically detects Glaucoma disease in human eye from the fundus database images. The feature extraction within images is done using Gray Level Difference Method (GLDM) and the classification is done by trained Artificial Neural Network. In this work, we achieved an accuracy of 86.66% with sensitivity at 93.33% and specificity at 80%.