Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
928-932,950

DR classification methods for DenseNet and SeNet fusion residue structures

Song Pengfeia,b
Wu Yuna,b
a. State Key Laboratory of Public Big Data, b. College of Computer Science & Technology, Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0305

Abstract

Diabetic retinopathy is the symptom of diabetes affecting the retina during the onset of diabetes. Aiming at the problem of information loss of lesion areas such as microimages during model downsampling, this paper proposed a module of DenseNet fusion residual structure. This module firstly connected two consecutive dense blocks, then sumed the feature information using the residual structure, and processed the feature image information in parallel to prevent the loss of effective feature information. Finally, the residual connected the two convolution blocks containing drop out to suppress the overfitting phenomenon. To solve the problem of the channel weighting of the feature graphs of lesion areas in previous convolution operations, this paper proposed a module of SeNet fusion residue structure. This module firstly connected SeNet, added the feature information of global average pooling and global maximum pooling to improve the utilization of effective channel information, and then ensured the integrity of feature graph information through the residual mode of conv1×1. Based on the design of the above two modules, this paper proposed a DR classification method of DenseNet and SeNet fusion residue structures. The model achieves 89.8% precision and 97.0% specificity on the APTOS2019 dataset, 78.8% accuracy and 91.9% specificity on the Messidor-2 dataset, which can effectively improve the classification ability of the degree of retinal lesions.

Foundation Support

贵州省科技计划资助项目(黔科合基础-ZK［2022］一般119)
贵州大学研究生创新人才计划项目
糖尿病视网膜图像分割分级模型研究的创新型人才培养实践

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0305
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Technology of Graphic & Image
Pages: 928-932,950
Serial Number: 1001-3695(2024)03-043-0928-05

Publish History

[2023-09-06] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

宋鹏飞, 吴云. DenseNet和SeNet融合残差结构的DR分类方法 [J]. 计算机应用研究, 2024, 41 (3): 928-932,950. (Song Pengfei, Wu Yun. DR classification methods for DenseNet and SeNet fusion residue structures [J]. Application Research of Computers, 2024, 41 (3): 928-932,950. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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