Published Paper Details:

A Chronic eye disorder called glaucoma leading to irreversible blindness by damaging the optic nerve of the eye. It is provoked due to exalted intraocular pressure inside the eye. Detecting glaucoma is the most challenging process in case of open angle glaucoma (OAG) due to lack of initial symptoms. Detecting glaucoma in the early stage is required to facilitate appropriate monitoring, treatment, and to diminish the likelihood of vision loss. In this paper, we propose a method to analyse and categorize the fundus image as glaucomatous or healthy image by employing image processing techniques to take into account the cup to disc ratio and feature extracted through Deep learning. The first step in identifying glaucoma is to measure CDR, which rises from 0.6 to 0.9 in cases of the condition. The Deep Learning-Convolution neural network model is used to automate the glaucoma diagnostic procedure and take into account additional medical data. Overfitting is avoided by adopting data augmentation technique. To make the system user friendly and interactive Graphical user interface (GUI) application is developed. The system is trained, and the outcomes show that the method performed well in categorizing the fundus images as healthy or glaucoma.