Breast Cancer Histological Image Classification with Multiple Features and Random Subspace Classifier Ensemble
Studies in computational intelligencePublished 13 December 2012
Yungang Zhang, Bailing Zhang, Wenjin Lu
Citations14
SJR quartileQ4
SJR score0.19
SNIP0.29
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TL;DR
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%.
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%.
Keywords
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