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21 July 2017 An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine
Lili Zhao, Jianping Yin, Lihuan Yuan, Qiang Liu, Kuan Li, Minghui Qiu
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104203U (2017) https://doi.org/10.1117/12.2281648
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
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.
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Lili Zhao, Jianping Yin, Lihuan Yuan, Qiang Liu, Kuan Li, and Minghui Qiu "An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203U (21 July 2017); https://doi.org/10.1117/12.2281648
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KEYWORDS
Image processing
Machine learning
Medical imaging
Pattern recognition
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