Enhanced CNN-RNN Deep Learning Based Framework for the Detection of Glaucoma

Abstract

Glaucoma is the currently leading retinal disease, which damages the eye due to the intraocular pressure on the eye. If the disease is not diagnosed in the early stage, then there is a chance to lose the vision. Mainly the progression of glaucoma will be examined on the retinal part of the eye by an experienced ophthalmologist. The manual detection of glaucoma is very tedious, and also it consumes more time. Hence this problem can be solved by automatically detecting glaucoma by applying deep learning techniques. In this paper, deep learning-based enhanced image segmentation, and classification approaches are used to get more accurate results. The modified convolutional neural network (CNN) architecture is applied to segment the fundus images of the optic cup (OC) and the optic disk (OD) part to calculate the cup-to-disc ratio (CDR) of the optic nerve head (ONH). The trained segmented images of the CNN model are applied for the enhanced RNN-LSTM model to classify the images from glaucoma and non-glaucoma images. DRISHTI_GS public database is used to test and train the model.