Experiments using an evolutionary programmed neural network with adaptive boosting for computer aided diagnosis of breast cancer

This paper extend ongoing CAD breast cancer research based on an evolutionary programmed neural network with adaptive boosting using a reduced set of discriminators for the Duke University and University of South Florida (USF) data sets. Early detection of non-palpable lesions by mammography can lead to reduced mortality. The high false positive rate of mammography has motivated CAD research efforts to improve its positive predictive value (PPV). Trials in which the discriminating features were reduced from 16 clinical patient history features to 7 are presented. In order to explore where discrimination improvements can be achieved, we define three approaches. Examining the reduced data sets revealed new patterns of conflicting data, where different outcomes for the same pattern of input features make the data sets inconsistent, contributing to performance degradation. This was confirmed by using decision trees to recursively partition the input space based on data-driven splitting criteria. We then describe modeling challenges posed by the mammography features, which are linguistic categorical variables, well suited to modeling by neuro-fuzzy paradigms. A series of comparative trials using the adaptive neuro-fuzzy inference system (ANFIS) is presented. Guided by these rules, two additional simplification strategies were implemented -reduction of the patient age state set and lesion variable reduction, imparting improved classification performance. At 100% sensitivity, where the clinical imperative of missing no cancer is met, specificity improved by 230% for the EP/AB Hybrid and by 76% for the ANFIS model. These results suggest that the number of benign outcome biopsies for suspicious mass lesions detected by mammography can be effectively reduced, while additionally reducing patient morbidity and overall healthcare cost.