Classification of breast abnormalities in digital mammograms using image and BI-RADS features in conjunction with neural network

This paper investigates the significance of combining grey-level based image features and BI-RADS lesion descriptors along with patient age and a subtlety value (radiologists' interpretation) for the reliable classification of calcification and mass type breast abnormalities into malignant and benign classes. Three sets of experiments using grey-level based image features, BI-RADS features and combined features were conducted on DDSIM benchmark database. The classification rate 91% on mass dataset and 74% on calcification dataset was obtained when both types of features combined together.