Mass characterization in mammograms using an optimal ensemble classifier

Mass is the most common indicator in mammograms, especially in the early stages of breast cancer. Due to subtle nature of the masses, there is a considerable overlap between the malignant and benign mass characteristics. In this work, a Computer Aided Diagnosis (CADx) system that employs ensemble classifier has been proposed to improve mass characterization. Genetic algorithm (GA), an optimization technique, was employed to select the optimal ensemble. Multicollinearity among classifiers has to be resolved while forming the ensemble. Combining the classifiers that are highly correlated will not guarantee an improved performance when compared to individual classifiers. Variation Inflation Factor (VIF) analysis is incorporated in this work for detecting multicollinearity among classifiers.