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

Yungang Zhang; Bailing Zhang; Wenjin Lu

doi:10.1063/1.3596623

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

Yungang Zhang^*
, Bailing Zhang
, Wenjin Lu

^*Corresponding author for this work

Xi'an Jiaotong-Liverpool University
University of Liverpool

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

14 Citations (Scopus)

Abstract

Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and 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	2011 International Symposium on Computational Models for Life Sciences, CMLS-11
Pages	19-28
Number of pages	10
DOIs	https://doi.org/10.1063/1.3596623
Publication status	Published - 2011
Event	2011 International Symposium on Computational Models for Life Sciences, CMLS-11 - Toyama City, Japan Duration: 11 Oct 2011 → 13 Oct 2011

Publication series

Name	AIP Conference Proceedings
Volume	1371
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2011 International Symposium on Computational Models for Life Sciences, CMLS-11
Country/Territory	Japan
City	Toyama City
Period	11/10/11 → 13/10/11

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

10.1063/1.3596623

Cite this

@inproceedings{d43c64b84d68455f9291a1b4bba82ed3,

title = "Breast cancer classification from histological images 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 breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and 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 = "2011",

doi = "10.1063/1.3596623",

language = "English",

isbn = "9780735409316",

series = "AIP Conference Proceedings",

pages = "19--28",

booktitle = "2011 International Symposium on Computational Models for Life Sciences, CMLS-11",

note = "2011 International Symposium on Computational Models for Life Sciences, CMLS-11 ; Conference date: 11-10-2011 Through 13-10-2011",

}

Zhang, Y, Zhang, B & Lu, W 2011, Breast cancer classification from histological images with multiple features and random subspace classifier ensemble. in 2011 International Symposium on Computational Models for Life Sciences, CMLS-11. AIP Conference Proceedings, vol. 1371, pp. 19-28, 2011 International Symposium on Computational Models for Life Sciences, CMLS-11, Toyama City, Japan, 11/10/11. https://doi.org/10.1063/1.3596623

Breast cancer classification from histological images with multiple features and random subspace classifier ensemble. / Zhang, Yungang; Zhang, Bailing; Lu, Wenjin.
2011 International Symposium on Computational Models for Life Sciences, CMLS-11. 2011. p. 19-28 (AIP Conference Proceedings; Vol. 1371).

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

TY - GEN

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

AU - Zhang, Yungang

AU - Zhang, Bailing

AU - Lu, Wenjin

PY - 2011

Y1 - 2011

N2 - Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and 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 breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and 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/79960107073

U2 - 10.1063/1.3596623

DO - 10.1063/1.3596623

M3 - Conference Proceeding

AN - SCOPUS:79960107073

SN - 9780735409316

T3 - AIP Conference Proceedings

SP - 19

EP - 28

BT - 2011 International Symposium on Computational Models for Life Sciences, CMLS-11

T2 - 2011 International Symposium on Computational Models for Life Sciences, CMLS-11

Y2 - 11 October 2011 through 13 October 2011

ER -

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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