Detection of microaneurysms in fundus images based on improved YOLOv4 with SENet embedded.
10.7507/1001-5515.202203022
- Author:
Weiwei GAO
1
;
Mingtao SHAN
1
;
Nan SONG
2
;
Bo FAN
1
;
Yu FANG
1
Author Information
1. Institute of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, P. R. China.
2. Eye & ENT Hospital of Fudan University, Shanghai 200031, P. R. China.
- Publication Type:Journal Article
- Keywords:
Fundus image;
Improved and fast fuzzy c-means;
Microaneurysms;
Squeeze-and-Excitation networks;
You only look once v4
- MeSH:
Algorithms;
Diabetic Retinopathy/diagnostic imaging*;
Fundus Oculi;
Humans;
Microaneurysm/diagnostic imaging*
- From:
Journal of Biomedical Engineering
2022;39(4):713-720
- CountryChina
- Language:Chinese
-
Abstract:
Microaneurysm is the initial symptom of diabetic retinopathy. Eliminating this lesion can effectively prevent diabetic retinopathy in the early stage. However, due to the complex retinal structure and the different brightness and contrast of fundus image because of different factors such as patients, environment and acquisition equipment, the existing detection algorithms are difficult to achieve the accurate detection and location of the lesion. Therefore, an improved detection algorithm of you only look once (YOLO) v4 with Squeeze-and-Excitation networks (SENet) embedded was proposed. Firstly, an improved and fast fuzzy c-means clustering algorithm was used to optimize the anchor parameters of the target samples to improve the matching degree between the anchors and the feature graphs; Then, the SENet attention module was embedded in the backbone network to enhance the key information of the image and suppress the background information of the image, so as to improve the confidence of microaneurysms; In addition, an spatial pyramid pooling was added to the network neck to enhance the acceptance domain of the output characteristics of the backbone network, so as to help separate important context information; Finally, the model was verified on the Kaggle diabetic retinopathy dataset and compared with other methods. The experimental results showed that compared with other YOLOv4 network models with various structures, the improved YOLOv4 network model could significantly improve the automatic detection results such as F-score which increased by 12.68%; Compared with other network models and methods, the automatic detection accuracy of the improved YOLOv4 network model with SENet embedded was obviously better, and accurate positioning could be realized. Therefore, the proposed YOLOv4 algorithm with SENet embedded has better performance, and can accurately and effectively detect and locate microaneurysms in fundus images.
