In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Graphic & Image
|
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. )

About the Journal

  • 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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)