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An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine
Author(s): Lili Zhao; Jianping Yin; Lihuan Yuan; Qiang Liu; Kuan Li; Minghui Qiu
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Paper Abstract

Automatic detection of abnormal cells from cervical smear images is extremely demanded in annual diagnosis of women’s cervical cancer. For this medical cell recognition problem, there are three different feature sections, namely cytology morphology, nuclear chromatin pathology and region intensity. The challenges of this problem come from feature combination s and classification accurately and efficiently. Thus, we propose an efficient abnormal cervical cell detection system based on multi-instance extreme learning machine (MI-ELM) to deal with above two questions in one unified framework. MI-ELM is one of the most promising supervised learning classifiers which can deal with several feature sections and realistic classification problems analytically. Experiment results over Herlev dataset demonstrate that the proposed method outperforms three traditional methods for two-class classification in terms of well accuracy and less time.

Paper Details

Date Published: 21 July 2017
PDF: 7 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203U (21 July 2017); doi: 10.1117/12.2281648
Show Author Affiliations
Lili Zhao, National Univ. of Defense Technology (China)
Jianping Yin, National Univ. of Defense Technology (China)
Lihuan Yuan, National Univ. of Defense Technology (China)
Qiang Liu, National Univ. of Defense Technology (China)
Kuan Li, National Univ. of Defense Technology (China)
Minghui Qiu, Chinese PLA General Hospital (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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