Diabetic retinopathy research based on deep converged network
10.3969/j.issn.1005-202X.2025.03.010
- VernacularTitle:基于深度融合网络研究糖尿病视网膜病变
- Author:
Ying ZHANG
1
;
Qiyang ZHAO
;
Qun XI
Author Information
1. 甘肃中医药大学信息工程学院,甘肃 兰州 730000
- Publication Type:Journal Article
- Keywords:
diabetic retinopathy;
deep learning;
deep residual shrinkage network;
pyramid split attention module
- From:
Chinese Journal of Medical Physics
2025;42(3):347-355
- CountryChina
- Language:Chinese
-
Abstract:
A converged network based on deep learning is proposed to realize the efficient and accurate diagnosis of diabetic retinopathy.Both data augmentation technology and generative adversarial network are used to augment the fundus images in EyePACS dataset for effectively addressing the problem of uneven classification of fundus images.The proposed model uses Inception-Resnet-V2 as the main network,and incorporates deep residual shrinkage network and pyramid split attention module for effectively filtering out the irrelevant information in the feature learning process and focusing on the lesion information,thereby improving the network's ability to capture important features.Experimental results show that the optimized model achieves accuracy,recall,specificity,sensitivity,and F1 score of 0.951,0.950,0.990,0.950,and 0.950,respectively,without the need to specify lesion characteristics in advance,demonstrating its superiority in evaluation indicators.
