Classification of glaucoma based on texture features using neural networks

Glaucoma is the most common cause of blindness and it affects most of the ageing society and this occurs due to pressure increases in the optic nerve which damages the optic nerve. This paper is an attempt to study and analyze the texture features of the Fundus image and its variations when the Fundus image is infected with glaucoma. The texture features extracted are localized around the optic cup which gives clear results for the purpose of distinct identification and classification. The classification method proposed is use of neural network classifier with the help of texture feature extraction of the localized area of the optic cup of the fundus images. The classification method gives high level of accuracy based on the different test-train ratios. The experimental results are encouraging indicating an accuracy of above 90% accuracy in classification.