Breast cancer histological image classification with multiple features and random subspace classifier ensemble

Yungang Zhang; Bailing Zhang; Wenjin Lu

doi:10.1007/978-3-642-33015-5_2

Breast cancer histological image classification with multiple features and random subspace classifier ensemble

Yungang Zhang
, Bailing Zhang
, Wenjin Lu

Xi'an Jiaotong-Liverpool University
University of Liverpool

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

18 Citations (Scopus)

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 Cooccurrence 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%.

Original language	English
Title of host publication	Knowledge-Based Systems in Biomedicine and Computational Life Science
Editors	Tuan D. Pham, Lakhmi C. Jain, Lakhmi C. Jain
Pages	27-42
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-642-33015-5_2
Publication status	Published - 2013

Publication series

Name	Studies in Computational Intelligence
Volume	450
ISSN (Print)	1860-949X

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Breast cancer classification
Curvelet transform
Histological images
Multilayer perceptron
Random subspace ensemble
Texture features

Access to Document

10.1007/978-3-642-33015-5_2

Cite this

Zhang, Y., Zhang, B., & Lu, W. (2013). Breast cancer histological image classification with multiple features and random subspace classifier ensemble. In T. D. Pham, L. C. Jain, & L. C. Jain (Eds.), Knowledge-Based Systems in Biomedicine and Computational Life Science (pp. 27-42). (Studies in Computational Intelligence; Vol. 450). https://doi.org/10.1007/978-3-642-33015-5_2

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title = "Breast cancer histological image classification with multiple features and random subspace classifier ensemble",

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 Cooccurrence 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 = "Breast cancer classification, Curvelet transform, Histological images, Multilayer perceptron, Random subspace ensemble, Texture features",

author = "Yungang Zhang and Bailing Zhang and Wenjin Lu",

year = "2013",

doi = "10.1007/978-3-642-33015-5\_2",

language = "English",

isbn = "9783642330148",

series = "Studies in Computational Intelligence",

pages = "27--42",

editor = "Pham, \{Tuan D.\} and Jain, \{Lakhmi C.\} and Jain, \{Lakhmi C.\}",

booktitle = "Knowledge-Based Systems in Biomedicine and Computational Life Science",

}

Breast cancer histological image classification with multiple features and random subspace classifier ensemble. / Zhang, Yungang; Zhang, Bailing; Lu, Wenjin.
Knowledge-Based Systems in Biomedicine and Computational Life Science. ed. / Tuan D. Pham; Lakhmi C. Jain; Lakhmi C. Jain. 2013. p. 27-42 (Studies in Computational Intelligence; Vol. 450).

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Breast cancer histological image classification with multiple features and random subspace classifier ensemble

AU - Zhang, Yungang

AU - Zhang, Bailing

AU - Lu, Wenjin

PY - 2013

Y1 - 2013

N2 - 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 Cooccurrence 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%.

AB - 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 Cooccurrence 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%.

KW - Breast cancer classification

KW - Curvelet transform

KW - Histological images

KW - Multilayer perceptron

KW - Random subspace ensemble

KW - Texture features

UR - https://www.scopus.com/pages/publications/84893146911

U2 - 10.1007/978-3-642-33015-5_2

DO - 10.1007/978-3-642-33015-5_2

M3 - Chapter

AN - SCOPUS:84893146911

SN - 9783642330148

T3 - Studies in Computational Intelligence

SP - 27

EP - 42

BT - Knowledge-Based Systems in Biomedicine and Computational Life Science

A2 - Pham, Tuan D.

A2 - Jain, Lakhmi C.

ER -

Breast cancer histological image classification with multiple features and random subspace classifier ensemble

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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