Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification

doi:10.1007/s11633-022-1354-z

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification
	Thisara Shyamalee; Dulani Meedeniya
刊名	Machine Intelligence Research
	2022
卷号	19 期号:6 页码:563-580
关键词	Attention U-Net segmentation classification Inception-v3 visual geometry group 19 (VGG19) residual neural network 50 (ResNet50) glaucoma fundus images
ISSN号	2731-538X
DOI	10.1007/s11633-022-1354-z
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55962]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	Department of Computer Science and Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka
推荐引用方式 GB/T 7714	Thisara Shyamalee,Dulani Meedeniya. Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification[J]. Machine Intelligence Research,2022,19(6):563-580.
APA	Thisara Shyamalee,&Dulani Meedeniya.(2022).Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification.Machine Intelligence Research,19(6),563-580.
MLA	Thisara Shyamalee,et al."Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification".Machine Intelligence Research 19.6(2022):563-580.