Using BI-RADS Descriptors and Ensemble Learning for Classifying Masses in Mammograms

This paper presents an ensemble learning approach for classifying masses in mammograms as malignant or benign by using Breast Image Report and Data System (BI-RADS) descriptors. We first identify the most important BI-RADS descriptors based on the information gain measure. Then we quantize the fine-grained categories of those descriptors into coarse-grained categories. Finally we apply an ensemble of multiple Machine Learning classification algorithms to produce the final classification. Experimental results showed that using the coarse-grained categories achieved equivalent accuracies compared with using the full fine-grained categories, and moreover the ensemble learning method slightly improved the overall classification. Our results indicate that automatic clinical decision systems can be simplified by focusing on coarsegrained BI-RADS categories without losing any accuracy for classifying masses in mammograms. Keyword: Mass Classification, BI-RADS, CADx.