  Automatic evaluation of the retinal fundus image is emerging as one of the
most important tools for early detection and treatment of progressive eye
diseases like Glaucoma. Glaucoma results to a progressive degeneration of
vision and is characterized by the deformation of the shape of optic cup and
the degeneration of the blood vessels resulting in the formation of a notch
along the neuroretinal rim. In this paper, we propose a deep learning-based
pipeline for automatic segmentation of optic disc (OD) and optic cup (OC)
regions from Digital Fundus Images (DFIs), thereby extracting distinct features
necessary for prediction of Glaucoma. This methodology has utilized focal notch
analysis of neuroretinal rim along with cup-to-disc ratio values as classifying
parameters to enhance the accuracy of Computer-aided design (CAD) systems in
analyzing glaucoma. Support Vector-based Machine Learning algorithm is used for
classification, which classifies DFIs as Glaucomatous or Normal based on the
extracted features. The proposed pipeline was evaluated on the freely available
DRISHTI-GS dataset with a resultant accuracy of 93.33% for detecting Glaucoma
from DFIs.
