In this paper, we present a unique two-stage classifier system for
identifying normal mammograms. We present methods that extract
features from breast regions characterizing normal and cancerous
tissue. A subset of the features is used to construct a classifier. This classifier is then used to classify each mammogram as normal or abnormal. We designed a unique two-stage cascading classifier system.
A binary decision tree classifier was used in the first stage. Cost constraints can be set to correctly classify cancerous regions. The regions classified as abnormal in the first-stage were used as input to the second-stage classifier, a linear classifier. We will show that the overall performance of our two-stage cascading classifier is better than a single classifier. Results of full-field normal mammogram analysis using this cascading classifier are comparable to a human reader.