Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000213-5.doi: 10.11896/jsjkx.211000213

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Classification Algorithm of Diabetic Retinopathy Based on Attention Mechanism

SUN Fu-quan1, ZOU Peng1,2, CUI Zhi-qing1,2, ZHANG Kun1   

  1. 1 School of Mathematics and Statistics,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066000,China
    2 School of Information Science and Engineering,Northeastern University,Shenyang 110000,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:SUN Fu-quan,born in 1964,Ph.D,postdoctoral fellow,professor.His main research interests include medical image processing and big data analysis.
    ZOU Peng,born in 1998,postgraduate.His main research interests include image processing and computer vision.
  • Supported by:
    National Key R & D Program of China(2018YFB1402800) and Hebei Higher Education Research and Practice Project(2018GJJG422).

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Abstract: Diabetic retinopathy is one of the important complications of diabetes and the main cause of blindness in the working population.The gap between retinal images is small and easy to be confused.Due to insufficient medical resources and lack of experienced ophthalmologists,it is difficult to carry out large-scale retinal image screening.Therefore,a classification algorithm for diabetic retinopathy based on attention mechanism is proposed to achieve accurate classification of the degree of retinal image lesions.The preprocessing operations such as data enhancement and image enhancement are carried out on data set.Using EfficientNetV2 as the backbone classification network,the attention mechanism is added to the network for fine-grained classification of retinal images,and the transfer learning strategy is used to train the network.The classification accuracy and the second weighted Kappa value of the proposed model are 97.8% and 0.843 respectively,which can effectively classify the disease degree of retinal images.Compared with other models,it has advantages and is of great significance for the diagnosis and treatment of diabetic retinopathy.

Key words: Deep learning, Classification of diabetic retinopathy, Attention mechanism, Data preprocessing, Transfer learning

CLC Number: 

  • TP393
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