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 proceedingConference 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. 

LanguageEnglish
Title of host publicationSeventh International Conference on Image and Graphics (ICIG), 2013
Place of PublicationPiscataway, New Jersey
PublisherIEEE
Pages390-393
Number of pages4
ISBN (Print)9780769550503
DOIs
Publication statusPublished - 1 Dec 2013
Event2013 7th International Conference on Image and Graphics, ICIG 2013 - Qingdao, Shandong, United Kingdom
Duration: 26 Jul 201328 Jul 2013

Conference

Conference2013 7th International Conference on Image and Graphics, ICIG 2013
CountryUnited Kingdom
CityQingdao, Shandong
Period26/07/1328/07/13

Fingerprint

Support vector machines
Decision making

Keywords

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

Cite this

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 (pp. 390-393). [6643702] Piscataway, New Jersey: IEEE. https://doi.org/10.1109/ICIG.2013.84
Ren, Jinchang ; Wang, Zheng ; Sun, Meijun ; Soraghan, John. / Effective classification of microcalcification clusters using improved support vector machine with optimised decision making. Seventh International Conference on Image and Graphics (ICIG), 2013. Piscataway, New Jersey : IEEE, 2013. pp. 390-393
@inproceedings{884baed8afde4db593d9eca0670e6ebe,
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 = "12",
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",

}

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; Soraghan, John.

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 proceedingConference 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 - http://www.scopus.com/inward/record.url?scp=84891290991&partnerID=8YFLogxK

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

SN - 9780769550503

SP - 390

EP - 393

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

PB - IEEE

CY - Piscataway, New Jersey

ER -

Ren J, Wang Z, Sun M, Soraghan J. Effective classification of microcalcification clusters using improved support vector machine with optimised decision making. In Seventh International Conference on Image and Graphics (ICIG), 2013. Piscataway, New Jersey: IEEE. 2013. p. 390-393. 6643702 https://doi.org/10.1109/ICIG.2013.84