A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning

Qiangzhi Zhang; Huali Chang; Longzhong Liu; Anhua Li; Qinghua Huang

doi:10.1007/978-3-662-45652-1_3

A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning

Qiangzhi Zhang, Huali Chang, Longzhong Liu, Anhua Li, Qinghua Huang

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

10 Scopus citations

Abstract

The occurance of Breast cancer increases significantly in the modern world. Therefore, the importance of computer-aided recognition of breast tumors also increases in clinical diagnosis. This paper proposes a novel computer-aided diagnosis (CAD) method for the classification of breast lesions as benign or malignant tumors using the biclustering learning technique. The medical data is graded based on the sonographic breast imaging reporting with data system (BI-RADS) lexicon. In the biclustering learning, the training data is used to find significant grading patterns. The grading pattern being learned is then applied to the test data. The k-Nearest Neighbors (k-NN) classifier is used as the classifier of breast tumors. Experimental results demonstrate that the proposed method classifies breast tumors into benign and malignant effectively. This indicates that it could yield good performances in real applications.

Original language	English
Title of host publication	Machine Learning and Cybernetics - 13th International Conference, Proceedings
Editors	Xizhao Wang, Qiang He, Patrick P.K. Chan, Witold Pedrycz
Publisher	Springer Verlag
Pages	24-32
Number of pages	9
ISBN (Electronic)	9783662456514
DOIs	https://doi.org/10.1007/978-3-662-45652-1_3
State	Published - 2014
Externally published	Yes
Event	13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 - Lanzhou, China Duration: 13 Jul 2014 → 16 Jul 2014

Publication series

Name	Communications in Computer and Information Science
Volume	481
ISSN (Print)	1865-0929

Conference

Conference	13th International Conference on Machine Learning and Cybernetics, ICMLC 2014
Country/Territory	China
City	Lanzhou
Period	13/07/14 → 16/07/14

Keywords

BI-RADS
Biclustering
Breast cancer
Computer-aided diagnosis (CAD)
K-NN

UN SDGs

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

Access to Document

10.1007/978-3-662-45652-1_3

Cite this

Zhang, Q., Chang, H., Liu, L., Li, A., & Huang, Q. (2014). A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning. In X. Wang, Q. He, P. P. K. Chan, & W. Pedrycz (Eds.), Machine Learning and Cybernetics - 13th International Conference, Proceedings (pp. 24-32). (Communications in Computer and Information Science; Vol. 481). Springer Verlag. https://doi.org/10.1007/978-3-662-45652-1_3

Zhang, Qiangzhi ; Chang, Huali ; Liu, Longzhong et al. / A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning. Machine Learning and Cybernetics - 13th International Conference, Proceedings. editor / Xizhao Wang ; Qiang He ; Patrick P.K. Chan ; Witold Pedrycz. Springer Verlag, 2014. pp. 24-32 (Communications in Computer and Information Science).

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title = "A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning",

abstract = "The occurance of Breast cancer increases significantly in the modern world. Therefore, the importance of computer-aided recognition of breast tumors also increases in clinical diagnosis. This paper proposes a novel computer-aided diagnosis (CAD) method for the classification of breast lesions as benign or malignant tumors using the biclustering learning technique. The medical data is graded based on the sonographic breast imaging reporting with data system (BI-RADS) lexicon. In the biclustering learning, the training data is used to find significant grading patterns. The grading pattern being learned is then applied to the test data. The k-Nearest Neighbors (k-NN) classifier is used as the classifier of breast tumors. Experimental results demonstrate that the proposed method classifies breast tumors into benign and malignant effectively. This indicates that it could yield good performances in real applications.",

keywords = "BI-RADS, Biclustering, Breast cancer, Computer-aided diagnosis (CAD), K-NN",

author = "Qiangzhi Zhang and Huali Chang and Longzhong Liu and Anhua Li and Qinghua Huang",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",

year = "2014",

doi = "10.1007/978-3-662-45652-1_3",

language = "英语",

series = "Communications in Computer and Information Science",

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Zhang, Q, Chang, H, Liu, L, Li, A & Huang, Q 2014, A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning. in X Wang, Q He, PPK Chan & W Pedrycz (eds), Machine Learning and Cybernetics - 13th International Conference, Proceedings. Communications in Computer and Information Science, vol. 481, Springer Verlag, pp. 24-32, 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014, Lanzhou, China, 13/07/14. https://doi.org/10.1007/978-3-662-45652-1_3

A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning. / Zhang, Qiangzhi; Chang, Huali; Liu, Longzhong et al.
Machine Learning and Cybernetics - 13th International Conference, Proceedings. ed. / Xizhao Wang; Qiang He; Patrick P.K. Chan; Witold Pedrycz. Springer Verlag, 2014. p. 24-32 (Communications in Computer and Information Science; Vol. 481).

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

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T1 - A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning

AU - Zhang, Qiangzhi

AU - Chang, Huali

AU - Liu, Longzhong

AU - Li, Anhua

AU - Huang, Qinghua

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2014.

PY - 2014

Y1 - 2014

N2 - The occurance of Breast cancer increases significantly in the modern world. Therefore, the importance of computer-aided recognition of breast tumors also increases in clinical diagnosis. This paper proposes a novel computer-aided diagnosis (CAD) method for the classification of breast lesions as benign or malignant tumors using the biclustering learning technique. The medical data is graded based on the sonographic breast imaging reporting with data system (BI-RADS) lexicon. In the biclustering learning, the training data is used to find significant grading patterns. The grading pattern being learned is then applied to the test data. The k-Nearest Neighbors (k-NN) classifier is used as the classifier of breast tumors. Experimental results demonstrate that the proposed method classifies breast tumors into benign and malignant effectively. This indicates that it could yield good performances in real applications.

AB - The occurance of Breast cancer increases significantly in the modern world. Therefore, the importance of computer-aided recognition of breast tumors also increases in clinical diagnosis. This paper proposes a novel computer-aided diagnosis (CAD) method for the classification of breast lesions as benign or malignant tumors using the biclustering learning technique. The medical data is graded based on the sonographic breast imaging reporting with data system (BI-RADS) lexicon. In the biclustering learning, the training data is used to find significant grading patterns. The grading pattern being learned is then applied to the test data. The k-Nearest Neighbors (k-NN) classifier is used as the classifier of breast tumors. Experimental results demonstrate that the proposed method classifies breast tumors into benign and malignant effectively. This indicates that it could yield good performances in real applications.

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KW - Breast cancer

KW - Computer-aided diagnosis (CAD)

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BT - Machine Learning and Cybernetics - 13th International Conference, Proceedings

A2 - Wang, Xizhao

A2 - He, Qiang

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PB - Springer Verlag

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Y2 - 13 July 2014 through 16 July 2014

ER -

Zhang Q, Chang H, Liu L, Li A, Huang Q. A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning. In Wang X, He Q, Chan PPK, Pedrycz W, editors, Machine Learning and Cybernetics - 13th International Conference, Proceedings. Springer Verlag. 2014. p. 24-32. (Communications in Computer and Information Science). doi: 10.1007/978-3-662-45652-1_3

A computer-aided system for classification of breast tumors in ultrasound images via biclustering learning

Abstract

Publication series

Conference

Keywords

UN SDGs

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