Diabetic Retinopathy Classification Based on Bilinear Cross Attention Network  

作　　者：Zhiyuan Ren Chen Xing Zhiyuan Ren;Chen Xing(School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China)

机构地区：[1]School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

出　　处：《Journal of Computer and Communications》2023年第5期16-28,共13页电脑和通信（英文）

摘　　要：Computer-aided diagnostic systems can assist doctors in diagnosing and treating DR cases more effectively, thereby improving work efficiency, reducing the burden on doctors during examinations, and alleviating problems related to uneven distribution of medical resources and shortage of doctors. In this article, we propose a classification method for diabetic retinopathy based on a bilinear multi-attention network. This method uses two backbone networks to extract features, and cross-shares the features using two attention modules to further deepen feature extraction. The non-local attention module is added to address the limitations of traditional convolutional neural networks in capturing global information. By paying attention to highly correlated pathological areas globally, performance improvement can be achieved. We achieved an accuracy of 91.7% on the Messidor dataset.Computer-aided diagnostic systems can assist doctors in diagnosing and treating DR cases more effectively, thereby improving work efficiency, reducing the burden on doctors during examinations, and alleviating problems related to uneven distribution of medical resources and shortage of doctors. In this article, we propose a classification method for diabetic retinopathy based on a bilinear multi-attention network. This method uses two backbone networks to extract features, and cross-shares the features using two attention modules to further deepen feature extraction. The non-local attention module is added to address the limitations of traditional convolutional neural networks in capturing global information. By paying attention to highly correlated pathological areas globally, performance improvement can be achieved. We achieved an accuracy of 91.7% on the Messidor dataset.

关 键 词：Diabetic Retinopathy Deep Neural Network STYLING Deep Neural Network 

分 类 号：G63[文化科学—教育学]