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Scarcity of large publicly available retinal fundus image datasets forautomated glaucoma detection has been the bottleneck for successful applicationof artificial intelligence towards practical Computer-Aided Diagnosis (CAD). Afew small datasets that are available for research community usually sufferfrom impractical image capturing conditions and stringent inclusion criteria.These shortcomings in already limited choice of existing datasets make itchallenging to mature a CAD system so that it can perform in real-worldenvironment. In this paper we present a large publicly available retinal fundusimage dataset for glaucoma classification called G1020. The dataset is curatedby conforming to standard practices in routine ophthalmology and it is expectedto serve as standard benchmark dataset for glaucoma detection. This databaseconsists of 1020 high resolution colour fundus images and provides ground truthannotations 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 alsoreport baseline results by conducting extensive experiments for automatedglaucoma diagnosis and segmentation of optic disc and optic cup.

G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection