Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs-Reference-Cited by-同舟云学术

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Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs

Published:2020-01-27 Issue:4 Volume:258 Page:851-867
ISSN:0721-832X
Container-title:Graefe's Archive for Clinical and Experimental Ophthalmology
language:en
Short-container-title:Graefes Arch Clin Exp Ophthalmol

Author:

Li Feng,Yan Lei,Wang Yuguang,Shi Jianxun,Chen Hua,Zhang Xuedian,Jiang Minshan,Wu Zhizheng,Zhou Kaiqian

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Cellular and Molecular Neuroscience,Sensory Systems,Ophthalmology

Link

http://link.springer.com/content/pdf/10.1007/s00417-020-04609-8.pdf

Reference36 articles.

1. Al-Bander B, Williams BM, Al-Nuaimy W et al (2018) Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry-Basel 10(4):87. https://doi.org/10.3390/sym10040087

2. Bock R, Meier J, Nyul LG et al (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14(3):471–481. https://doi.org/10.1016/j.media.2009.12.006

3. Tham YC, Li X, Wong TY et al (2014) Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-ayalysis. Ophthalmology 121(11):2081–2090. https://doi.org/10.1016/j.ophtha.2014.05.013

4. Quigley HA, Broman AT (2006) The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 90(3):262–267. https://doi.org/10.1136/bjo.2005.081224

5. Orlando JI, Prokofyeva E, Fresno M et al (2017) Convolutional neural network transfer for automated glaucoma identification. Proceeding of SPIE 10160:UNSP 101600U. https://doi.org/10.1117/12.2255740

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