An Automated Early Detection of Glaucoma using Support Vector Machine Based Visual Geometry Group 19 (VGG-19) Convolutional Neural Network

Abstract

Deep learning is a useful technique for investigating the medicinal images. Glaucoma is a neurotic condition, dynamic neuro degeneration of the optic nerve, which leads visual impairment. It could be forestalled by an early detection of glaucoma and the regular screening with specialist for glaucoma diagnosis. Glaucoma is assessed by observing intra ocular pressure and optic Cup-Disc-Ratio (CDR). In this paper, novel mechanized glaucoma recognition has been performed by utilizing computer supported analysis from fundus images. The simulation outcomes are acquired by utilizing a Support Vector Machine based VGG-19 network architecture. The CDR threshold value of 0.41 has been used for glaucoma recognition. The fundus images which has the CDR greater than 0.41 is treated as glaucoma affected and less than 0.41 is non-glaucoma fundus images. The proposed glaucoma recognition system works with reasonable to obtain and generally utilized digital color fundus images. For the set of 175 fundus images a classification precision of 94% has been accomplished.