  With the advancement of powerful image processing and machine learning
techniques, CAD has become ever more prevalent in all fields of medicine
including ophthalmology. Since optic disc is the most important part of retinal
fundus image for glaucoma detection, this paper proposes a two-stage framework
that first detects and localizes optic disc and then classifies it into healthy
or glaucomatous. The first stage is based on RCNN and is responsible for
localizing and extracting optic disc from a retinal fundus image while the
second stage uses Deep CNN to classify the extracted disc into healthy or
glaucomatous. In addition to the proposed solution, we also developed a
rule-based semi-automatic ground truth generation method that provides
necessary annotations for training RCNN based model for automated disc
localization. The proposed method is evaluated on seven publicly available
datasets for disc localization and on ORIGA dataset, which is the largest
publicly available dataset for glaucoma classification. The results of
automatic localization mark new state-of-the-art on six datasets with accuracy
reaching 100% on four of them. For glaucoma classification we achieved AUC
equal to 0.874 which is 2.7% relative improvement over the state-of-the-art
results previously obtained for classification on ORIGA. Once trained on
carefully annotated data, Deep Learning based methods for optic disc detection
and localization are not only robust, accurate and fully automated but also
eliminates the need for dataset-dependent heuristic algorithms. Our empirical
evaluation of glaucoma classification on ORIGA reveals that reporting only AUC,
for datasets with class imbalance and without pre-defined train and test
splits, does not portray true picture of the classifier's performance and calls
for additional performance metrics to substantiate the results.
