Effective classification of microcalcification clusters using improved support vector machine with optimised decision making

Jinchang Ren; Zheng Wang; Meijun Sun; John Soraghan

doi:10.1109/ICIG.2013.84

Effective classification of microcalcification clusters using improved support vector machine with optimised decision making

Jinchang Ren, Zheng Wang, Meijun Sun, John Soraghan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

Abstract

Classification of micro calcification clusters is very essential for early detection of breast cancer from mammograms. In this paper, an improved support vector machine (SVM) scheme is proposed, where optimized decision making is introduced for effective and more accurate data classification. Experimental results on the well-known DDSM database have shown that the proposed method can significantly increase the performance in terms of F1 and Az measurements for the successful classification of clustered micro calcifications.

Original language	English
Title of host publication	Seventh International Conference on Image and Graphics (ICIG), 2013
Place of Publication	Piscataway, New Jersey
Publisher	IEEE
Pages	390-393
Number of pages	4
ISBN (Print)	9780769550503
DOIs	https://doi.org/10.1109/ICIG.2013.84
Publication status	Published - 1 Dec 2013
Event	2013 7th International Conference on Image and Graphics, ICIG 2013 - Qingdao, Shandong, United Kingdom Duration: 26 Jul 2013 → 28 Jul 2013

Conference

Conference	2013 7th International Conference on Image and Graphics, ICIG 2013
Country/Territory	United Kingdom
City	Qingdao, Shandong
Period	26/07/13 → 28/07/13

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

computer-aided diagnosis
mammography
microcalification clusters (MCC)
optimized decision making
support vector machine (SVM)

Access to Document

10.1109/ICIG.2013.84

Cite this

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title = "Effective classification of microcalcification clusters using improved support vector machine with optimised decision making",

abstract = "Classification of micro calcification clusters is very essential for early detection of breast cancer from mammograms. In this paper, an improved support vector machine (SVM) scheme is proposed, where optimized decision making is introduced for effective and more accurate data classification. Experimental results on the well-known DDSM database have shown that the proposed method can significantly increase the performance in terms of F1 and Az measurements for the successful classification of clustered micro calcifications. ",

keywords = "computer-aided diagnosis, mammography, microcalification clusters (MCC), optimized decision making, support vector machine (SVM)",

author = "Jinchang Ren and Zheng Wang and Meijun Sun and John Soraghan",

year = "2013",

month = dec,

day = "1",

doi = "10.1109/ICIG.2013.84",

language = "English",

isbn = "9780769550503",

pages = "390--393",

booktitle = "Seventh International Conference on Image and Graphics (ICIG), 2013",

publisher = "IEEE",

note = "2013 7th International Conference on Image and Graphics, ICIG 2013 ; Conference date: 26-07-2013 Through 28-07-2013",

}

Ren, J, Wang, Z, Sun, M & Soraghan, J 2013, Effective classification of microcalcification clusters using improved support vector machine with optimised decision making. in Seventh International Conference on Image and Graphics (ICIG), 2013., 6643702, IEEE, Piscataway, New Jersey, pp. 390-393, 2013 7th International Conference on Image and Graphics, ICIG 2013, Qingdao, Shandong, United Kingdom, 26/07/13. https://doi.org/10.1109/ICIG.2013.84

Effective classification of microcalcification clusters using improved support vector machine with optimised decision making. / Ren, Jinchang; Wang, Zheng; Sun, Meijun et al.
Seventh International Conference on Image and Graphics (ICIG), 2013. Piscataway, New Jersey: IEEE, 2013. p. 390-393 6643702.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Effective classification of microcalcification clusters using improved support vector machine with optimised decision making

AU - Ren, Jinchang

AU - Wang, Zheng

AU - Sun, Meijun

AU - Soraghan, John

PY - 2013/12/1

Y1 - 2013/12/1

N2 - Classification of micro calcification clusters is very essential for early detection of breast cancer from mammograms. In this paper, an improved support vector machine (SVM) scheme is proposed, where optimized decision making is introduced for effective and more accurate data classification. Experimental results on the well-known DDSM database have shown that the proposed method can significantly increase the performance in terms of F1 and Az measurements for the successful classification of clustered micro calcifications.

AB - Classification of micro calcification clusters is very essential for early detection of breast cancer from mammograms. In this paper, an improved support vector machine (SVM) scheme is proposed, where optimized decision making is introduced for effective and more accurate data classification. Experimental results on the well-known DDSM database have shown that the proposed method can significantly increase the performance in terms of F1 and Az measurements for the successful classification of clustered micro calcifications.

KW - computer-aided diagnosis

KW - mammography

KW - microcalification clusters (MCC)

KW - optimized decision making

KW - support vector machine (SVM)

UR - https://www.scopus.com/pages/publications/84891290991

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6642427

U2 - 10.1109/ICIG.2013.84

DO - 10.1109/ICIG.2013.84

M3 - Conference contribution book

AN - SCOPUS:84891290991

SN - 9780769550503

SP - 390

EP - 393

BT - Seventh International Conference on Image and Graphics (ICIG), 2013

PB - IEEE

CY - Piscataway, New Jersey

T2 - 2013 7th International Conference on Image and Graphics, ICIG 2013

Y2 - 26 July 2013 through 28 July 2013

ER -

Effective classification of microcalcification clusters using improved support vector machine with optimised decision making

Abstract

Conference

UN SDGs

Keywords

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