天津科技大学学报2024，Vol.39Issue(2)：59-64,6.DOI:10.13364/j.issn.1672-6510.20230080
基于多任务学习的青光眼智能诊断
Intelligent Diagnosis of Glaucoma Based on Multi-Task Learning
摘要
Abstract
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.关键词
青光眼诊断/图像分割/图像分类/多任务学习Key words
diagnosis of glaucoma/image segmentation/image classification/multi-task learning分类
信息技术与安全科学引用本文复制引用
魏宏博,武劲圆,陈磊,冯梓毅,游国栋..基于多任务学习的青光眼智能诊断[J].天津科技大学学报,2024,39(2):59-64,6.基金项目
天津市科技支撑重点项目(17YFZCNC00230) （17YFZCNC00230）
天津市应用基础与前沿技术研究计划(自然科学基金)重点项目(13JCZDJC29100) （自然科学基金）
