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

Ren, Jinchang and Wang, Zheng and Sun, Meijun and Soraghan, John; (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. IEEE, GBR, pp. 390-393. ISBN 9780769550503 (https://doi.org/10.1109/ICIG.2013.84)

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.

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• Item metadata

  Item type:	Book Section
  ID code:	48589
  Dates:	Date Event 1 December 2013 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering Science > Mathematics > Electronic computers. Computer science
  Department:	Faculty of Engineering > Electronic and Electrical Engineering Technology and Innovation Centre > Sensors and Asset Management University of Strathclyde > University of Strathclyde
  Depositing user:	Pure Administrator
  Date deposited:	17 Jun 2014 09:33
  Last modified:	08 Apr 2024 13:12
  Related URLs:	Scopus publication Journal or Publication
  URI:	https://strathprints.strath.ac.uk/id/eprint/48589