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Automatic Detection Algorithm Based on LVQ Neural Network for Micro-calcification Clusters

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Human Centered Computing (HCC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8944))

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Abstract

Given that the micro-calcification clusters in early breast cancer X-ray pictures are minimal and irregular with differentiated shapes and distributions as well as the unsatisfactorily low contrast ratio, micro-calcification clusters of small sizes and the unsatisfactorily low contrast ratio tend to be easily ignored or misdiagnosed by doctors. This paper applies the LVQ Neural Network to classify micro-calcification clusters as malignant or benign in digitized mammograms based on feature extraction of statistical methods. The result shows the method of LVQ Neural Network is simpler and effective.

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Author information

Authors and Affiliations

  1. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Liuhe Road No. 318, Hangzhou, Zhejiang, 310023, China

    Ying Kong

  2. College of Information Engineering, China Jiliang University, Hangzhou, China

    Hui-Juan Lu

Authors
  1. Ying Kong
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  2. Hui-Juan Lu
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Corresponding author

Correspondence to Ying Kong .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe, Middlesex, United Kingdom

    Bo Hu

  3. Fudan University, Shanghai, China

    Ning Gu

  4. Telecommunications and Information Communication Technology, National Institute of Posts, Phnom Penh, Cambodia

    Sopheap Seng

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Kong, Y., Lu, HJ. (2015). Automatic Detection Algorithm Based on LVQ Neural Network for Micro-calcification Clusters. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_47

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  • DOI: https://doi.org/10.1007/978-3-319-15554-8_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15553-1

  • Online ISBN: 978-3-319-15554-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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