Mammographic mass classification using wavelet based support vector machine

Abstract

In this paper, we investigate an approach for classification of mammographic masses as benign ormalign. This study relies on a combination of Support Vector Machine (SVM) and wavelet-basedsubband image decomposition. Decision making was performed in two stages as feature extraction bycomputing the wavelet coefficients and classification using the classifier trained on the extractedfeatures. SVM, a learning machine based on statistical learning theory, was trained throughsupervised learning to classify masses. The research involved 66 digitized mammographic images.The masses were segmented manually by radiologists, prior to introduction to the classificationsystem. Preliminary test on mammogram showed over 84.8% classification accuracy by using theSVM with Radial Basis Function (RBF) kernel. Also confusion matrix, accuracy, sensitivity andspecificity analysis with different kernel types were used to show the classification performance ofSVM.