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In this manuscript, we present a robust method for glaucoma screening fromfundus images using an ensemble of convolutional neural networks (CNNs). Thepipeline comprises of first segmenting the optic disk and optic cup from thefundus image, then extracting a patch centered around the optic disk andsubsequently feeding to the classification network to differentiate the imageas diseased or healthy. In the segmentation network, apart from the image, wemake use of spatial co-ordinate (X \& Y) space so as to learn the structure ofinterest better. The classification network is composed of a DenseNet201 and aResNet18 which were pre-trained on a large cohort of natural images. On theREFUGE validation data (n=400), the segmentation network achieved a dice scoreof 0.88 and 0.64 for optic disc and optic cup respectively. For the taskingdifferentiating images affected with glaucoma from healthy images, the areaunder the ROC curve was observed to be 0.85.

Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images using Position encoded CNNs