Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231000042-5.doi: 10.11896/jsjkx.231000042

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Grading Model for Diabetic Retinopathy Based on Graph Convolutional Network

YANG Yufan, YUAN Liming, WANG Ke, LI Hongyi, LI Yixuan, YAO Yujia, WANG Jingyi   

  1. School of Computer Science and Engineering,Tianjin University of Technology,Tianjin 300384 ,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:YANG Yufan,born in 2002,undergra-duate.His main research interests include machine learning and deep lear-ning.
    YUAN Liming,born in 1982,Ph.D,associate professor,postgraduate supervisor.His main research interests include machine learning and medical image analysis.
  • Supported by:
    Program of Undergraduate Innovation and Entrepreneurship Training in Tianjin University of Technology(202110060108) and Program of Research and Reform on Graduate Education and Teaching in Tianjin University of Technology(YBXM2318).

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Abstract: Diabetic retinopathy is a high-risk blinding disease.If it is detected early,it can be treated to slow or stop further vision loss in patients.There have been some successful cases of using deep learning to conduct diabetic retinal disease detection.Nevertheless,these methods usually only consider the spatial relationship between pixels in images and do not take into account the relationship between deeper features of the images.For this reason,a graph convolutional network based diabetic retinopathy gra-ding model is proposed with the aim of helping doctors and researchers to better grade and diagnose diabetic retinopathy images in clinical practice and scientific research.This model mainly uses the graph convolutional network to capture the important grading information embedded among deep features of an image,to obtain features with stronger semantic information,and to construct a two-way branching network based on it.In addition,for better feature fusion,an adaptive weighting mechanism is used to further improve the grading performance.Experimental results show that the proposed method can fully learn the relationship between the deep features of the image by using the graph convolutional network,so as to improve the classification performance,and its classification accuracy reaches about 84.8% on the APTOS2019 dataset and about 68% on the Messidor-2 dataset.

Key words: Diabetic retinopathy grading, Convolutional neural network, Graph convolutional network, Two-way branch network, Adaptive weighting mechanism

CLC Number: 

  • TP391
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