Picture of smart phone in human hand

World leading smartphone and mobile technology research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

Explore Strathclyde's Open Access research on smartphone technology now...

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, Piscataway, New Jersey, pp. 390-393. ISBN 9780769550503

Full text not available in this repository. (Request a copy from the Strathclyde author)

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.