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

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

    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.

    Original languageEnglish
    Title of host publicationNinth International Conference on Digital Image Processing, ICDIP 2017
    EditorsXudong Jiang, Charles M. Falco
    PublisherSPIE
    Volume10420
    ISBN (Electronic)9781510613041
    DOIs
    Publication statusPublished - 1 Jan 2017
    Event9th International Conference on Digital Image Processing, ICDIP 2017 - Hong Kong, China
    Duration: 19 May 201722 May 2017

    Conference

    Conference9th International Conference on Digital Image Processing, ICDIP 2017
    Country/TerritoryChina
    CityHong Kong
    Period19/05/1722/05/17

    Keywords

    • abnormal cervical cell detection
    • feature combination
    • Medical image analysis
    • multi-instance extreme learning machine

    Fingerprint

    Dive into the research topics of 'An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine'. Together they form a unique fingerprint.

    Cite this