Automated detection of Glaucoma using deep learning convolution network (G-net) Mamta Juneja 1 & Shaswat Singh 1 & Naman Agarwal 1 & Shivank Bali 1 & Shubham Gupta 1 & Niharika Thakur 1 & Prashant Jindal 1 Received: 1 October 2018 /Revised: 7 January 2019 /Accepted: 7 March 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Glaucoma is an ocular disease that is the leading cause of irreversible blindness due to an increased Intraocular pressure resulting in damage to the optic nerve of eye. A common method for diagnosing glaucoma progression is through examination of dilated pupil in the eye by expert ophthalmologist. But this approach is laborious and consumes a large amount of time, thus the issue can be resolved using automation by using the concept of machine learning. Convolution neural networks (CNN’ s) are well suited to resolve this class of problems as they can infer hierarchical information from the image which helps them to distinguish between glaucomic and non-glaucomic image patterns for diagnostic decisions. This paper presents an Artificially Intelligent glaucoma expert system based on segmentation of optic disc and optic cup. A Deep Learning architecture is developed with CNN working at its core for automating the detection of glaucoma. The proposed system uses two neural networks working in conjunction to segment optic cup and disc. The model was tested on 50 fundus images and achieved an accuracy of 95.8% for disc and 93% for cup segmentation. Keywords Glaucoma detection . Optic disc . Optic cup . Retinal fundus image . Neural network . Image segmentation 1 Introduction Glaucoma, also known as “silent thief of sight” is the second leading cause of blindness across the world. Over 60 million people suffer from glaucoma globally and by 2020, this number is expected to rise to 79.6 million [ 27]. It is characterized by the malfunctioning and loss of ganglion cells, which results in the change of the structure of the optic nerve head, the thickness of retinal nerve fiber layer, ganglion cell, and inner plexiform layers. Without proper treatment, glaucoma can cause irreversible damage to Multimedia Tools and Applications https://doi.org/10.1007/s11042-019-7460-4 * Prashant Jindal jindalp@pu.ac.in Extended author information available on the last page of the article