Deep Segmentation Architecture with Self Attention for Glaucoma Detection

Glaucoma is one of the disorders that infects the retinal. All people are exposed to infection by Glaucoma, but age people most commonly affect them and lead to loss of vision. Unfortunately, there is no Glaucoma medication yet, but the good news is, early detection of it prevents further vision loss or blindness. The traditional diagnose of Glaucoma faced many challenges like a long time, less of ophthalmologists in the remote area, and difficulty detection Glaucoma in the early stage of it. Therefore, clinical diagnosis has been combined with computer vision techniques. In this paper, we suggest a deep learning method based on Cup-to-disc ratio measures for the detection of Glaucoma. We used Encoder-Decoder with Atrous Convolution and a Self-attention mechanism, which allows modeling a long-range dependency across image regions. The experimental results of this method are proved in the REFUGE dataset.