An Approach Based on Immune Algorithm and SVM for Detection and Classification of Microcalcifications

As the feature based detection and classification of microcalcifications (MCs) in digital mammograms is considered here as a machine-leaning problem, we investigate an approach using immune algorithm (IA) and support vector machine (SVM), called IA-SVM, to solve it. Firstly, because only support vectors (SVs) are needed to build the classification hyperplane, we compress the training set according to their intra-class and inter-class Euclidean distances without losing any SVs. Meanwhile, an IA based MCs´ features selector is provided to select an optimal feature subset, which can construct the input vectors for the latter SVM training; Secondly, the compressed and optimized training samples are fed to a SVM based classifier to make the optimal classification hyperplane more efficiently and more effectively. Experiments demonstrate that our method has better computing performance than other traditional classifiers (training samples were compressed by about 15%) and yields a satisfying A_z value (about 0.83).