Picture of athlete cycling

Open Access research with a real impact on health...

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 Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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.