  Glaucoma is a major eye disease, leading to vision loss in the absence of
proper medical treatment. Current diagnosis of glaucoma is performed by
ophthalmologists who are often analyzing several types of medical images
generated by different types of medical equipment. Capturing and analyzing
these medical images is labor-intensive and expensive. In this paper, we
present a novel computational approach towards glaucoma diagnosis and
localization, only making use of eye fundus images that are analyzed by
state-of-the-art deep learning techniques. Specifically, our approach leverages
Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation
Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively.
Quantitative and qualitative results, as obtained for a small-sized dataset
with no segmentation ground truth, demonstrate that the proposed approach is
promising, for instance achieving an accuracy of 0.91$\pm0.02$ and an ROC-AUC
score of 0.94 for the diagnosis task. Furthermore, we present a publicly
available prototype web application that integrates our predictive model, with
the goal of making effective glaucoma diagnosis available to a wide audience.
