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)
Full text not available in this repository.Request a copyAbstract
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.
ORCID iDs
Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194, Wang, Zheng, Sun, Meijun and Soraghan, John ORCID: https://orcid.org/0000-0003-4418-7391;-
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Item type: Book Section ID code: 48589 Dates: DateEvent1 December 2013PublishedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
University of Strathclyde > University of StrathclydeDepositing user: Pure Administrator Date deposited: 17 Jun 2014 09:33 Last modified: 11 Nov 2024 14:56 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/48589