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

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Published Date: Jan 27, 2020

Abstract

PURPOSE: To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuropathy (GON) using color fundus images, understand the process by which the model makes predictions, and explore the effect of the integration of fundus images and the medical history data from patients.

Authors

  • Feng Li
    Department of General Surgery, Shanghai Traditional Chinese Medicine (TCM)-INTEGRATED Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Lei Yan
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Yuguang Wang
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Jianxun Shi
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Hua Chen
    Management College, Beijing Union University, Beijing, China.
  • Xuedian Zhang
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Minshan Jiang
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. jiangmsc@gmail.com.
  • Zhizheng Wu
    Department of Precision Mechanical Engineering, Shanghai University, Shanghai, 200072, China.
  • Kaiqian Zhou
    Liver Cancer Institute, Zhongshan Hospital, Shanghai, 200032, China.

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