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Early detection of glaucoma is important for slowing disease progression and preventing total vision loss. The diagnosis of glaucoma is closely related to the shape of the optic disc and cup (cup-disc) and whether there is a defect in the retinal nerve fiber layer (RNFL). In previous studies, it was common to predict glaucoma by analyzing changes in cup-to-disc ratio, or to directly classify fundus images for glaucoma using a deep learning classification model. This paper proposes a method for diagnosing glaucoma by combining the cup-disc shape information and retinal nerve fiber layer defect (RNFLD) information. We use a fully convolutional neural network that based on a multi-scale attention mechanism (AM-CNN） to identify cup-disc morphology and RNFLD regions, further use previous methods and image processing methods to extract features in these two spaces. Finally, we use the SVM method in machine learning to classify the sample for glaucoma based on the features fusion of the two spaces. Specifically, we first establish a small database with both the cup-disc annotation, retinal nerve fiber layer defect annotation and glaucoma diagnosis results, which includes 735 fundus images labeled with either positive glaucoma (356) or negative glaucoma (379). Then, a semantic segmentation model based on attention is designed. By adding attention to the context information of the model, a more accurate segmentation image is obtained, not only has a good effect on the segmentation of the cup-disc, but also has a significant effect on the recognition of RNFLDs. Finally, the four-dimensional features were extracted from the cup-disc segmentation map by the previous method, and the four-dimensional features such as distance and area were extracted from the retinal nerve fiber layer segmentation map. Combine the two kinds of features using SVM algorithm to establish a classification model for glaucoma classification. The experiment results show that adding the attention module to the decoder can improve the effect of segmentation tasks for more complex problems and the classification model fusion cup-disc shape and RNFLD information significantly advances glaucoma detection.
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