  In this manuscript, we present a robust method for glaucoma screening from
fundus images using an ensemble of convolutional neural networks (CNNs). The
pipeline comprises of first segmenting the optic disk and optic cup from the
fundus image, then extracting a patch centered around the optic disk and
subsequently feeding to the classification network to differentiate the image
as diseased or healthy. In the segmentation network, apart from the image, we
make use of spatial co-ordinate (X \& Y) space so as to learn the structure of
interest better. The classification network is composed of a DenseNet201 and a
ResNet18 which were pre-trained on a large cohort of natural images. On the
REFUGE validation data (n=400), the segmentation network achieved a dice score
of 0.88 and 0.64 for optic disc and optic cup respectively. For the tasking
differentiating images affected with glaucoma from healthy images, the area
under the ROC curve was observed to be 0.85.
