A diagnostic method incorporating multi-scale feature fusion and hybrid domain attention mechanism for fundus diseases
10.3969/j.issn.1005-202X.2023.12.005
- VernacularTitle:一种结合多尺度特征融合和混合域注意力机制的眼底疾病诊断方法
- Author:
Hui LIU
1
;
Zhengwei ZHU
;
Xu ZHANG
;
Hui ZHONG
Author Information
1. 西南科技大学信息工程学院,四川绵阳 621010
- Keywords:
fundus disease;
convolutional neural network;
multi-scale feature fusion;
hybrid domain attention mechanism
- From:
Chinese Journal of Medical Physics
2023;40(12):1477-1485
- CountryChina
- Language:Chinese
-
Abstract:
In view of numerous subtle features in fundus disease images,small sample sizes,and difficulties in diagnosis,both deep learning and medical imaging technologies are used to develop a fundus disease diagnosis model that integrates multi-scale features and hybrid domain attention mechanism.Resnet50 network is taken as the baseline network,and it is modified in the study.The method uses parallel multi-branch architecture to extract the features of fundus diseases under different receptive fields for effectively improving the feature extraction ability and computational efficiency,and adopts hybrid domain attention mechanism to select information that is more critical to the current task for effectively enhancing the classification performance.The test on ODIR dataset shows that the proposed method has a diagnostic accuracy of 93.2%for different fundus diseases,which is 5.2%higher than the baseline network,demonstrating a good diagnostic performance.
