@inproceedings{8aca129012b34c29bad6f52732a2d408,
title = "Intelligent identification of microscopic visible components in leucorrhea routine",
abstract = "Leucorrhea routine is a common way of female physiological examination, which is detected by recognizing and counting the visible components in microscopic images. At present, the research in this field is still blank. Based on the deep learning theory, an improved R-CNN model is proposed to realize the intelligent recognition of the visible components in leucorrhea microscopic images. The detection precision of the algorithm is high, reaching 93.6%, and the detection time is 300 ms. The proposed algorithm provides a theoretical basis for the realization of leucorrhea routine automation and intellectualization.",
keywords = "Deep learning, Leucorrhea microscopic image, Object detection, R-CNN",
author = "Xiaohui Du and Lin Liu and Xiangzhou Wang and Guangming Ni and Jing Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019 ; Conference date: 15-03-2019 Through 17-03-2019",
year = "2019",
month = mar,
doi = "10.1109/ITNEC.2019.8729545",
language = "English",
series = "Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2344--2349",
editor = "Bing Xu",
booktitle = "Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019",
address = "United States",
}