Comparisons of feature selection methods using discrete wavelet transforms and Support Vector Machines for mammogram images

Abstract

In this paper, we investigate wavelet-based feature extraction from mammogram images and efficient dimensionality reduction techniques. The aim is to propose a new computerized feature extraction technique to identify abnormalities in breast mammogram images. In this work, dimensionality reduction is carried out using the minimal-redundancy-maximal-relevance criterion (mRMR). The classification accuracy for each set of features is measured and evaluated using machine learning techniques and support vector machines (SVMs).