Computer aided breast cancer diagnosis: an SVM-based mammogram classification approach

Abstract

This paper addresses the problem of mammogram classification (benign vs. malignant) through the utilisation of the state-of-the-art machine learning paradigm of support vector machines (SVMs). We are particularly interested in evaluating the discrimination efficiency of a set of feature extraction algorithms that have been proposed in the relevant literature for describing the textual characteristics present in mammogram masses. Our research focuses on comparing the classification accuracy associated with two well-established feature generation methodologies, namely, spatial grey level dependence method (SGLDM) and run difference method (RDM). Our experimentation was conducted on a publicly available mammogram database by parameterising the underlying kernel function of SVMs on different subsets of features. Our results indicate a moderately high classification accuracy for the linear SVM classifier when trained on the complete set of features.