Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network

Presence of mass in breast tissues is highly indicative of breast cancer. The research work investigates the significance of neural-association of mass type of breast abnormality patterns for benign and malignant class characterization using auto-associator neural network and original features. The characterized patterns are finally classified into benign and malignant classes using a classifier neural network. Grey-level based statistical features, BIRADS features, patient age feature and subtlety value feature have been used in proposed research work. The proposed research technique attained a 94% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.