Glaucoma is a chronic eye disease that leads to irreversible vision loss. In this chapter, we introduce two state-of-the-art glaucoma detection methods based on deep learning technique. The first is the multi-label segmentation network, named M-Net, which solves the optic disc and optic cup segmentation jointly. M-Net contains a multi-scale U-shape convolutional network with the side-output layer to learn discriminative representations and produces segmentation probability map. Then the vertical cup to disc ratio (CDR) is calculated based on segmented optic disc and cup to assess the glaucoma risk. The second network is the disc-aware ensemble network, named DENet, 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. The DENet produces the glaucoma detection result from the image directly without segmentation. Finally, we compare two deep learning methods with other related methods on several glaucoma detection datasets.