  Taking into account that glaucoma is the leading cause of blindness
worldwide, we propose in this paper three different learning methodologies for
glaucoma detection in order to elucidate that traditional machine-learning
techniques could outperform deep-learning algorithms, especially when the image
data set is small. The experiments were performed on a private database
composed of 194 glaucomatous and 198 normal B-scans diagnosed by expert
ophthalmologists. As a novelty, we only considered raw circumpapillary OCT
images to build the predictive models, without using other expensive tests such
as visual field and intraocular pressure measures. The results ratify that the
proposed hand-driven learning model, based on novel descriptors, outperforms
the automatic learning. Additionally, the hybrid approach consisting of a
combination of both strategies reports the best performance, with an area under
the ROC curve of 0.85 and an accuracy of 0.82 during the prediction stage.
