Automatic detection of fungi in microscopic leucorrhea images based on convolutional neural network and morphological method

Ruqian Hao, Xiangzhou Wang, Jing Zhang, Juanxiu Liu, Xiaohui Du, Lin Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Leucorrhea routine test is one of the most widely used tests in gynecological examinations, and fungi inspection is vital for gynecological test because fungi is an important evidence for fungal vaginitis. In order to improve detection accuracy, an automatic identification of fungi in microscopic leucorrhea images based on convolutional neural network (CNN) and morphological method is proposed in this paper. First, we use the maximum inter-class variance method to segment original image and obtain possible fungi subimages. Then, a fully trained CNN is applied to recognize fungi. Finally, morphological method, such as template match method and concave point detection method, is used to further classify the selected candidate to improve recognize accuracy. In experiments, the method using CNN and morphological method achieved 93.26% accuracy.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
EditorsBing Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2491-2494
Number of pages4
ISBN (Electronic)9781538662434
DOIs
Publication statusPublished - Mar 2019
Externally publishedYes
Event3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 - Chengdu, China
Duration: 15 Mar 201917 Mar 2019

Publication series

NameProceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019

Conference

Conference3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
Country/TerritoryChina
CityChengdu
Period15/03/1917/03/19

Keywords

  • Convolutional neural network
  • Fungi detection
  • Morphological method

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Cite this

Hao, R., Wang, X., Zhang, J., Liu, J., Du, X., & Liu, L. (2019). Automatic detection of fungi in microscopic leucorrhea images based on convolutional neural network and morphological method. In B. Xu (Ed.), Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 (pp. 2491-2494). Article 8729396 (Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNEC.2019.8729396