  Glaucoma is a chronic eye disease that leads to irreversible vision loss.
Most of the existing automatic screening methods firstly segment the main
structure, and subsequently calculate the clinical measurement for detection
and screening of glaucoma. However, these measurement-based methods rely
heavily on the segmentation accuracy, and ignore various visual features. In
this paper, we introduce a deep learning technique to gain additional
image-relevant information, and screen glaucoma from the fundus image directly.
Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic
glaucoma screening is proposed, which integrates the deep hierarchical context
of the global fundus image and the local optic disc region. Four deep streams
on different levels and modules are respectively considered as global image
stream, segmentation-guided network, local disc region stream, and disc polar
transformation stream. Finally, the output probabilities of different streams
are fused as the final screening result. The experiments on two glaucoma
datasets (SCES and new SINDI datasets) show our method outperforms other
state-of-the-art algorithms.
