Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model.

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Published Date: Dec 7, 2019

Abstract

PURPOSE: To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (HCFs) based on spectral domain optical coherence tomography (SD-OCT) peripapillary retinal nerve fiber layer (pRNFL) images.

Authors

  • Ce Zheng
    Department of Ophthalmology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Xiaolin Xie
    Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China.
  • Longtao Huang
    Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
  • Binyao Chen
    Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China.
  • Jianling Yang
    Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China.
  • Jiewei Lu
    Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
  • Tong Qiao
    Department of Ophthalmology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Zhun Fan
  • Mingzhi Zhang
    Joint Shantou International Eye Center, Shantou University & the Chinese University of Hong Kong, Shantou, China. Electronic address: zmz@jsiec.org.

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