  Scarcity of large publicly available retinal fundus image datasets for
automated glaucoma detection has been the bottleneck for successful application
of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A
few small datasets that are available for research community usually suffer
from impractical image capturing conditions and stringent inclusion criteria.
These shortcomings in already limited choice of existing datasets make it
challenging to mature a CAD system so that it can perform in real-world
environment. In this paper we present a large publicly available retinal fundus
image dataset for glaucoma classification called G1020. The dataset is curated
by conforming to standard practices in routine ophthalmology and it is expected
to serve as standard benchmark dataset for glaucoma detection. This database
consists of 1020 high resolution colour fundus images and provides ground truth
annotations for glaucoma diagnosis, optic disc and optic cup segmentation,
vertical cup-to-disc ratio, size of neuroretinal rim in inferior, superior,
nasal and temporal quadrants, and bounding box location for optic disc. We also
report baseline results by conducting extensive experiments for automated
glaucoma diagnosis and segmentation of optic disc and optic cup.
