Automatic Glaucoma Diagnosis in Digital Fundus images using Convolutional Neural Network

The World Health Organization (WHO) approximates that more than 42 million people are currently blind in the world, 80 percent of which could have been prevented or cured by early detection. According to a survey, Glaucoma is the second most leading cause for blindness after cataract. It is an irreversible eye disease, once the vision is lost it can not be recovered. Thus, it is vital to develop an automatic computerised tool to diagnose the disease. In this paper, an oveland robust deep learning based convolutional neural networks(CNN) architecture has been proposed to deal with the problem. The network consists of six convolutional layers, with various activation functions, and pooling layers to get the abstract and detailed information of the input image. The proposed architecture predicts the probability of an image being Glaucoma. The model has been experimented with Refugee and Drishti datasets. Our proposed model is able to diagnose the Glaucoma disease automatically with an accuracy of 95%, sensitivity of 100%, and specificity of 90% respectively.