Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

Abstract

Feature selection and classifier hyper-parameter optimization are important stages of any computer-aided diagnosis (CADx) system for mammography. The optimal selection for shape features, kernel parameter, and classifier regularization constant is crucial to achieve a good generalization and performance of least-squares support vector machines (LSSVMs). This paper presents a morphology-based CADx that uses a computationally attractive and unified scheme for accomplishing the model selection task.A heuristic parameter search based on particle swarm optimization (PSO) not only reduces the dimensionality of the input feature space but also optimizes hyper-parameters of the classifier. The performance of the proposed shape-based CADx including PSO-LSSVM parameter selection method is examined using 60 microcalcification clusters. Using different cross-validation procedures, the proposed PSO-LSSVM demonstrated a good generalization ability by producing classification accuracies higher than 92%. The best classification accuracy of 97% was obtained using the leave-one-out cross-validation procedure. Comparing the performance of PSO-LSSVM with PSO-SVM method that uses conventional SVM formulation, results demonstrated the attractive computational complexity and classification performance of PSO-LSSVM.