Ant colony Optimization for Feature Selection and Classification of Microcalcifications in Digital Mammograms

Genetic algorithm (GA) and Ant colony optimization (ACO) 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 ant colony optimization (BPN-ACO) 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.

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