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    Knowledge-Discovery Incorporated Evolutionary Search for Microcalcification Detection in Breast Cancer Diagnosis.

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    Publication date
    2006
    Author
    Peng, Yonghong
    Yao, Bin
    Jiang, Jianmin
    Keyword
    Knowledge-discovery
    Genetic algorithm
    Microcalcification detection
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    Objectives The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size. Methods and materials This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly. Results and conclusions The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.
    URI
    http://hdl.handle.net/10454/3270
    Version
    No full-text available in the repository
    Citation
    Peng, Y., Yao, B. and Jiang, J. (2006). Knowledge-Discovery Incorporated Evolutionary Search for Microcalcification Detection in Breast Cancer Diagnosis. Artificial Intelligence in Medicine. Vol. 37, No. 1, pp. 43-53.
    Link to publisher’s version
    http://dx.doi.org/10.1016/j.artmed.2005.09.001
    Type
    Article
    Collections
    Engineering and Informatics Publications

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