An Efficient Method for Early Glaucoma Screening based on Calculating Optic Cup to Disc Ratio Using Convolutional Neural Networks

Document Type : Research Paper


1 Master of Science in Telecommunication Systems, Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran

2 Department of Electrical Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran


Early diagnosis and treatment of glaucoma can prevent the progression of this disease and the sudden loss of vision. Glaucoma affects the optic disc and optic cup located inside the optic disc. In this paper, first, the optic disc is localized and then segmentation of optic disc and cup is performed to diagnose based on the optic cup to disc ratio (CDR). A Faster Region-based Convolutional Neural Network (Faster-RCNN) with the pre-trained ResNet50 network is used for the optic disc localization step. The segmentation step is performed by the modified U-Net architecture using the SE-ResNet50 network as its encoding layers, and finally CDR is evaluated. The Drishti-GS1 and RIM-ONE v3 databases are used to train and test the proposed method and the MESSIDOR database is only used in the test phase. In addition, for segmentation of optic disc and cup, two approaches are proposed to consider the optic disc and cup annotations in the Drishti-GS1 data set’s ground truth. In the second proposed approach and according to the F1-score criteria, the result of optic cup and disc segmentation for Drishti-GS1 data set is 0.93 and 0.97, respectively, for RIM-ONE v3 data set is 0.79 and 0.95, respectively, and for MESSIDOR data set is 0.84 and 0.93, respectively, which is competitive with other works.