Intelligent Diagnosis of Glaucoma Based on Multi-Task Learning
In order to enhance the accuracy of glaucoma detection and mitigate the risks associated with glaucoma,in this article we propose an intelligent diagnostic method for glaucoma based on multi-task learning.Our proposed method com-bines the U-Net and VGG16 networks,with the encoder part of the U-Net network being shared by both networks.By util-izing the U-Net network,the cup-to-disc ratio(CDR)is obtained from retinal images,and this CDR is used as one of the features input into the VGG16 network to achieve glaucoma classification for the retinal images.The proposed method was validated using the REFUGE challenge datasets.After training the network model,the area under the receiver operating characteristic curve(AUC)was measured to be 0.9788.Moreover,the segmentation accuracy for the optic disc and optic cup was found to be 0.8745 and 0.9624,respectively.In comparison to other methods using the same datasets,the proposed method in this article demonstrates higher accuracy in glaucoma classification.
diagnosis of glaucomaimage segmentationimage classificationmulti-task learning
国家科技期刊平台
NSTL
万方数据
维普
