Discriminative feature selection for breast abnormality detection and accurate classification of thermograms

Abstract

Infrared breast thermography with the potential of predicting the future risk of developing breast cancer, has been considered as an early breast abnormality detection tool. This paper investigates the importance of selecting the discriminative features for improving the classification accuracy of the infrared thermography based breast abnormality detection systems. Mann-Whitney-Wilcoxon statistical test has been used here to select the best discriminative features from a feature set of 24 features, extracted from each breast thermogram of DBT-TU-JU and DMR databases. Three set of features: FStat, STex and SSigFS generated from these 24 extracted features are then fed into six most widely used classifiers for comparing the efficiency of each feature set in breast abnormality detection. The experimental results show that among all three feature sets, statistically significant feature set (SSigFS) provides more accuracy in discriminating the abnormal breast thermograms from the normal.