A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images

Abstract

Glaucoma is currently leading retinal disease, which damages the eye because of the Intraocular pressure (IOP) on the eye. If glaucoma is left untreated it will lead to vision loss by damaging the Optic Nerve Head (ONH). The progression of glaucoma is examined on the retinal part of the eye by an experienced ophthalmologist. This approach is very tedious, and it consumes more time to do it manually. Hence this issue is right problem that can be solved by automatically diagnosing glaucoma with the help of the deep learning approaches. Convolutional Neural Networks (CNN's) are appropriate to find the solution for this type of issue as they can extract various levels of data from the input image, and which encourages to differentiate among non-glaucomic and glaucomic images. This proposed paper introduces an efficient glaucoma master framework to segment the optic cup and optic disc to find the Cup-to-Disc-Ratio (CDR). Here the diagnosis of glaucoma is achieved by using deep learning with novel CNN. The proposed system uses two individual CNN architecture to segment the Optic Cup (OC) and Optic Disc (OD) to get more accurate result. This model is trained and tested on DRISHTI – GS database, which is publicly available and an accuracy of 98% for optic disc and 97% for optic cup segmentation is achieved.