A new approach for vaginal microbial micrograph classification using convolutional neural network combined with decision-making tree (CNN-DMT)

Kongya Zhao; Hao He; Peng Gao; Sunxiangyu Liu; Xinyan Zhang; Guitao Li; Youzheng Wang

doi:10.1117/12.2524209

6 May 2019 A new approach for vaginal microbial micrograph classification using convolutional neural network combined with decision-making tree (CNN-DMT)

Kongya Zhao, Hao He, Peng Gao, Sunxiangyu Liu, Xinyan Zhang, Guitao Li, Youzheng Wang

Author Affiliations +

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110692U (2019) https://doi.org/10.1117/12.2524209
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

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%.

Citation Download Citation

Kongya Zhao, Hao He, Peng Gao, Sunxiangyu Liu, Xinyan Zhang, Guitao Li, and Youzheng Wang "A new approach for vaginal microbial micrograph classification using convolutional neural network combined with decision-making tree (CNN-DMT)", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692U (6 May 2019); https://doi.org/10.1117/12.2524209

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KEYWORDS

Photomicroscopy

Convolutional neural networks

Image classification

Medical imaging

Bacteria

Diagnostics

Mathematical modeling

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