Journal of Guangxi Normal University(Natural Science Edition) ›› 2024, Vol. 42 ›› Issue (1): 91-101.doi: 10.16088/j.issn.1001-6600.2023051805

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Classification of Diabetic Retinopathy Based on Split Residual Network

XIAO Yuting1, LÜ Xiaoqi1,2*, GU Yu1, LIU Chuanqiang1   

  1. 1. Key Laboratory of Pattern Recognition and Intelligent Image Processing (Inner Mongolia University of Science and Technology), Baotou Neimenggu 014010, China;
    2. School of Information Engineering, Inner Mongolia University of Technology, Hohhot Neimenggu 010051, China
  • Received:2023-05-18 Revised:2023-09-13 Online:2024-01-25 Published:2024-01-19

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Abstract: Diabetic retinopathy is a common complication of diabetes. A classification algorithm based on split residual network is proposed to improve the classification accuracy of diabetic retinopathy images. Through the fusion of normalized attention, the ability to identify key feature information is enhanced, which makes the model more targeted for extracting lesion feature information. The global context module is used to comprehensively consider the feature information learned from different scales and network layers, and further contact the characteristics of diabetic retinopathy in different periods to enhance the expression ability of the model. The output structure uses multi-branch structure to classify images, which avoids the problem that the uneven distribution of data sets affects the classification accuracy. Experimental results show that the model accuracy is 94.86%, and other evaluation indexes are improved compared with the original backbone network model. The proposed model has good performance, and the high-precision diagnosis and grading of diabetic retina images are realized.

Key words: medical image processing, deep learning, diabetic retinopathy, attention mechanism, split residual network

CLC Number:  TP391.41
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