Semi-supervised classification of fundus images combined with CNN and GCN.

Journal: Journal of applied clinical medical physics
Published Date: Aug 10, 2022

Abstract

PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can greatly save clinicians' diagnosis time. To alleviate these problems, in this paper, we propose a novel framework-graph attentional convolutional neural network (GACNN).

Authors

  • Sixu Duan
    Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, China.
  • Pu Huang
    Department of Obstetrics & Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, Xian, Shaanxi, China.
  • Min Chen
    School of Computer Science and TechnologyHuazhong University of Science and Technology Wuhan 430074 China.
  • Ting Wang
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
  • Xiaolei Sun
    Eye Hospital of Shandong First Medical University (Shandong Eye Hospital), Jinan, China.
  • Meirong Chen
    Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Xueyuan Dong
    Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan, China.
  • Zekun Jiang
    College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
  • Dengwang Li
    Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China.

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