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Knowledge-Based Systems in Biomedicine and Computational Life Science pp 27–42Cite as

Breast Cancer Histological Image Classification with Multiple Features and Random Subspace Classifier Ensemble

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 450))

Abstract

Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breast cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurrence Matrix (GLCM) and the Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Software Engineering, Xi’an JiaoTong-Liverpool University, Suzhou, 215123, P.R. China

    Yungang Zhang, Bailing Zhang & Wenjin Lu

  2. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom

    Yungang Zhang

Authors
  1. Yungang Zhang
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  2. Bailing Zhang
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  3. Wenjin Lu
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Editor information

Editors and Affiliations

  1. Aizu Research Cluster for Medical Eng, and Informatics Center for Advanced, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan

    Tuan D. Pham

  2. , Adjunct Professor, University of Canberra, Kirinari St. Mail Room, ACT, 2601, Australia

    Lakhmi C. Jain

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Zhang, Y., Zhang, B., Lu, W. (2013). Breast Cancer Histological Image Classification with Multiple Features and Random Subspace Classifier Ensemble. In: Pham, T., Jain, L. (eds) Knowledge-Based Systems in Biomedicine and Computational Life Science. Studies in Computational Intelligence, vol 450. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33015-5_2

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  • DOI: https://doi.org/10.1007/978-3-642-33015-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33014-8

  • Online ISBN: 978-3-642-33015-5

  • eBook Packages: EngineeringEngineering (R0)

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