New particle swarm optimization for feature selection and classification of microcalcifications in mammograms

An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications earlier and faster than typical screening programs. In this paper, Genetic Algorithm (GA) and New Particle Swarm Optimization (NPSO) algorithm are proposed for feature selection, and their performance is compared. The Spatial Gray Level Dependence Method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer Backpropagation Network hybrid with New Particle Swarm Optimization (BPN) for classification. And the Receiver Operating Characteristic (ROC) analysis is performed to evaluate the performance of the feature selection methods with their classification results. The proposed algorithms are tested with 114 abnormal images from the Mammography Image Analysis Society (MIAS) database.