Effective Classification of Microcalcification Clusters Using Improved Support Vector Machine with Optimised Decision Making

Jinchang Ren, Zheng Wang, Meijun Sun, John J. Soraghan

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.