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Wang, Q.L., Wu, B.G., Zhu, P.F., Li, P.H., Zuo, W.M. and Hu, Q.H. (2020) ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 13-19 June 2020, 11531-11539.
https://doi.org/10.1109/CVPR42600.2020.01155

has been cited by the following article:

• TITLE: Diabetic Retinopathy Classification Based on Bilinear Cross Attention Network

  AUTHORS: Zhiyuan Ren, Chen Xing

  KEYWORDS: Diabetic Retinopathy, Deep Neural Network, Styling, Deep Neural Network

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.5, May 25, 2023

  ABSTRACT: 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.