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

  • Yungang Zhang
  • Bailing Zhang
  • Wenjin Lu
Part of the Studies in Computational Intelligence book series (SCI, volume 450)


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%.


Breast cancer classification histological images curvelet transform texture features multilayer perceptron random subspace ensemble 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yungang Zhang
    • 1
    • 2
  • Bailing Zhang
    • 1
  • Wenjin Lu
    • 1
  1. 1.Department of Computer Science & Software EngineeringXi’an JiaoTong-Liverpool UniversitySuzhouP.R. China
  2. 2.Department of Computer ScienceUniversity of LiverpoolLiverpoolUnited Kingdom

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