典型文献
Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification
文献摘要：
Glaucoma is a prevalent cause of blindness worldwide.If not treated promptly,it can cause vision and quality of life to de-teriorate.According to statistics,glaucoma affects approximately 65 million individuals globally.Fundus image segmentation depends on the optic disc(OD)and optic cup(OC).This paper proposes a computational model to segment and classify retinal fundus images for glaucoma detection.Different data augmentation techniques were applied to prevent overfitting while employing several data pre-pro-cessing approaches to improve the image quality and achieve high accuracy.The segmentation models are based on an attention U-Net with three separate convolutional neural networks(CNNs)backbones:Inception-v3,visual geometry group 19(VGG19),and residual neural network 50(ResNet50).The classification models also employ a modified version of the above three CNN architectures.Using the RIM-ONE dataset,the attention U-Net with the ResNet50 model as the encoder backbone,achieved the best accuracy of 99.58％in seg-menting OD.The Inception-v3 model had the highest accuracy of 98.79％for glaucoma classification among the evaluated segmentation,followed by the modified classification architectures.
文献关键词：
中图分类号：
作者姓名：
Thisara Shyamalee;Dulani Meedeniya
作者机构：
Department of Computer Science and Engineering,University of Moratuwa,Katubedda 10400,Sri Lanka
文献出处：
引用格式：
[1]Thisara Shyamalee;Dulani Meedeniya-.Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification)[J].机器智能研究（英文）,2022(06):563-580
A类：
teriorate
B类：
Glaucoma,Detection,Retinal,Fundus,Images,Using,Segmentation,Classification,prevalent,cause,blindness,worldwide,If,not,treated,promptly,can,vision,quality,life,According,statistics,glaucoma,affects,approximately,million,individuals,globally,segmentation,depends,optic,disc,OD,cup,OC,This,paper,proposes,computational,classify,retinal,fundus,images,detection,Different,augmentation,techniques,were,applied,prevent,overfitting,while,employing,several,cessing,approaches,improve,accuracy,models,are,attention,three,separate,convolutional,neural,networks,CNNs,backbones,Inception,v3,visual,geometry,group,VGG19,residual,ResNet50,classification,also,modified,version,above,architectures,RIM,ONE,dataset,encoder,achieved,best,menting,had,highest,among,evaluated,followed,by
AB值：
0.608696
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