Using BI-RADS descriptors and ensemble learning for classifying masses in mammograms

Authors:
Yu Zhang;Noriko Tomuro;Jacob Furst;Daniela Stan Raicu
Affiliations:
College of Computing and Digital Media, DePaul University, Chicago, IL;College of Computing and Digital Media, DePaul University, Chicago, IL;College of Computing and Digital Media, DePaul University, Chicago, IL;College of Computing and Digital Media, DePaul University, Chicago, IL
Venue:
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Year:
2009

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Abstract

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 coarse-grained BI-RADS categories without losing any accuracy for classifying masses in mammograms.