  Glaucoma is one of the leading causes of blindness worldwide and Optical
Coherence Tomography (OCT) is the quintessential imaging technique for its
detection. Unlike most of the state-of-the-art studies focused on glaucoma
detection, in this paper, we propose, for the first time, a novel framework for
glaucoma grading using raw circumpapillary B-scans. In particular, we set out a
new OCT-based hybrid network which combines hand-driven and deep learning
algorithms. An OCT-specific descriptor is proposed to extract hand-crafted
features related to the retinal nerve fibre layer (RNFL). In parallel, an
innovative CNN is developed using skip-connections to include tailored residual
and attention modules to refine the automatic features of the latent space. The
proposed architecture is used as a backbone to conduct a novel few-shot
learning based on static and dynamic prototypical networks. The k-shot paradigm
is redefined giving rise to a supervised end-to-end system which provides
substantial improvements discriminating between healthy, early and advanced
glaucoma samples. The training and evaluation processes of the dynamic
prototypical network are addressed from two fused databases acquired via
Heidelberg Spectralis system. Validation and testing results reach a
categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively.
Besides, the high performance reported by the proposed model for glaucoma
detection deserves a special mention. The findings from the class activation
maps are directly in line with the clinicians' opinion since the heatmaps
pointed out the RNFL as the most relevant structure for glaucoma diagnosis.
