Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (6): 39-48.doi: 10.3969/j.issn.1000-5641.2023.06.004

• Computer Science • Previous Articles     Next Articles

Diabetic retinopathy grading based on dual-view image feature fusion

Lulu JIANG1,2, Siqi SUN3,4,*(), Haidong ZOU2,5,6, Lina LU2,6, Rui FENG1,2,3,4   

  1. 1. Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
    2. Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai 200080, China
    3. School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
    4. Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Fudan University, Shanghai 200433, China
    5. Shanghai General Hospital, Shanghai 200080, China
    6. Shanghai Eye Disease Prevention Center, Shanghai 200040, China
  • Received:2022-06-09 Online:2023-11-25 Published:2023-11-23
  • Contact: Siqi SUN


The diagnostic method based on dual-view fundus imaging is widely used in diabetic retinopathy (DR) screening. This method effectively solves the problems of image occlusion and limited field of view under single-view. This paper proposes a learning method of feature fusion between dual-view images based on the attention mechanism to improve the accuracy of DR classification by effectively integrating different view information. Due to the small proportion of lesions in fundus images, the self-attention mechanism was introduced to enhance the learning of local lesion features. Moreover, a cross-attention mechanism is proposed to effectively utilize information between dual-view images to improve the classification of dual-view fundus images. Experiments were performed on the internal DFiD dataset and public DeepDRiD dataset. The proposed method can effectively improve the accuracy of DR classification and can be used for large-scale DR screening to assist doctors in achieving an efficient diagnosis.

Key words: fundus image, feature fusion, dual-view image fusion, attention mechanism, diabetic retinopathy

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