Classification of Clustered Microcalcifications using MLFFBP-ANN and SVM

Abstract The classifier is the last phase of Computer-Aided Diagnosis (CAD) system that is aimed at classifying Clustered Microcalcifications (MCCs). Classifier classifies MCCs into two classes. One class is benign and other is malignant. This classification is done based on some meaningful features that are extracted from enhanced mammogram. A number of classifiers have been proposed for CAD system to classify MCCs as benign or malignant. Recently, researchers have used Artificial Neural Networks (ANNs) as classifiers for many applications. Multilayer Feed-Forward Backpropagation (MLFFB) is the most important ANN that has been successfully used by researchers to solve various problems. Similarly, Support Vector Machines (SVMs) belong to another category of classifiers that researchers have recently given considerable attention. So, to explore MLFFB and SVM classifiers for MCCs classification problem, in this paper, Levenberg-Marquardt Multilayer Feed-Forward Backpropagation ANN (LM-MLFFB-ANN) and Sequential Minimal Optimization (SMO) based SVM (SMO-SVM) are used for the classification of MCCs. Thus, a comparative evaluation of the relative performance of LM-MLFFBP-ANN and SMO-SVM is investigated to classify MCCs as benign or malignant. For this comparative evaluation, first suitable features are extracted from mammogram images of DDSM database. After this, suitable features are selected using Particle Swarm Optimization (PSO). At the end, MCCs are classified using LM-MLFFBP-ANN and SMO-SVM classifiers based on the selected features. Confusion matrix and ROC analysis are used to measure the performance of LM-MLFFBP-ANN and SMO-SVM classifiers. Experimental results indicate that the performance of SMO-SVM is better than that of LM-MLFFBP-ANN.