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A new approach for vaginal microbial micrograph classification using convolutional neural network combined with decision-making tree (CNN-DMT)
Author(s): Kongya Zhao; Hao He; Peng Gao; Sunxiangyu Liu; Xinyan Zhang; Guitao Li; Youzheng Wang
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Paper Abstract

Vaginitis, the most common disease of female genital tract infections, mainly relies on the morphological detection of the vaginal micro-ecological system to diagnose under the microscope. It affects women's normal life seriously, even their fertility. Since the morphological detection is very dependent on the experience of the observer, while the experienced doctors are mostly concentrated in large cities, the problem of diagnosis of vaginitis in rural women is extremely serious. Convolutional neural network (CNN), the typical algorithm of artificial intelligence, has shown great potential in many visual classification tasks. However, it is difficult to apply CNN method directly to the diagnosis of vaginitis. To solve the problem, this paper proposes an algorithm combining CNN with decision-making tree (CNNDMT) based on medical expert consensus. In a way of incorporating features automatically extracted by the machine and expert knowledge, automatic diagnosis of vaginitis disease is realized. Experimental results show that the CNN-DMT approach improves test accuracy by 8.46% over the leading CNN method, while enhancing the accuracy of normal bacterial flora by more than 15%.

Paper Details

Date Published: 6 May 2019
PDF: 8 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692U (6 May 2019); doi: 10.1117/12.2524209
Show Author Affiliations
Kongya Zhao, Tsinghua Univ. (China)
Beijing National Research Ctr. for Information Science and Technology (China)
Hao He, Tsinghua Univ. (China)
Beijing National Research Ctr. for Information Science and Technology (China)
Peng Gao, Tsinghua Univ. (China)
Beijing National Research Ctr. for Information Science and Technology (China)
Sunxiangyu Liu, Tsinghua Univ. (China)
Beijing National Research Ctr. for Information Science and Technology (China)
Xinyan Zhang, Tsinghua Univ. (China)
Beijing National Research Ctr. for Information Science and Technology (China)
Guitao Li, Tsinghua Univ. (China)
Beijing National Research Ctr. for Information Science and Technology (China)
Youzheng Wang, Tsinghua Univ. (China)
Beijing National Research Ctr. for Information Science and Technology (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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