  Automated Computer Aided diagnostic tools can be used for the early detection
of glaucoma to prevent irreversible vision loss. In this work, we present a
Multi-task Convolutional Neural Network (CNN) that jointly segments the Optic
Disc (OD), Optic Cup (OC) and predicts the presence of glaucoma in color fundus
images. The CNN utilizes a combination of image appearance features and
structural features obtained from the OD-OC segmentation to obtain a robust
prediction. The use of fewer network parameters and the sharing of the CNN
features for multiple related tasks ensures the good generalizability of the
architecture, allowing it to be trained on small training sets. The
cross-testing performance of the proposed method on an independent validation
set acquired using a different camera and image resolution was found to be good
with an average dice score of 0.92 for OD, 0.84 for OC and AUC of 0.95 on the
task of glaucoma classification illustrating its potential as a mass screening
tool for the early detection of glaucoma.
