TY - GEN
T1 - Automatic detection of fungi in microscopic leucorrhea images based on convolutional neural network and morphological method
AU - Hao, Ruqian
AU - Wang, Xiangzhou
AU - Zhang, Jing
AU - Liu, Juanxiu
AU - Du, Xiaohui
AU - Liu, Lin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Fungi detection
KW - Morphological method
UR - http://www.scopus.com/inward/record.url?scp=85067860824&partnerID=8YFLogxK
U2 - 10.1109/ITNEC.2019.8729396
DO - 10.1109/ITNEC.2019.8729396
M3 - Conference contribution
AN - SCOPUS:85067860824
T3 - Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
SP - 2491
EP - 2494
BT - Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
Y2 - 15 March 2019 through 17 March 2019
ER -