Adaptive Splitting and Selection Algorithm for Classification of Breast Cytology Images

Abstract

AbstractThe article presents an application of Adaptive Splitting and Selection (AdaSS) classifier in the medical decision support system for breast cancer diagnosis. Apart from the canonical malignant versus non-malignant problem we introduced a third class - fibroadenoma, which is a benign tumor of the breast often occurring in women. Medical images are delivered by the Regional Hospital in Zielona Góra, Poland. For the process of segmentation and feature extraction a mixture of Gaussians is used. AdaSS is a combined classifier, based on an evolutionary splitting of feature space into clusters. To increase the overall accuracy of the classification we propose to add a feature selection step to the optimization criterion of the native AdaSS algorithm. Experimental investigation proves that the introduced method is more accurate than previously used classification approaches.Keywordsmachine learningmultiple classifier systemclustering and selectionevolutionary algorithmbreast cancercomputer-aided diagnostics