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.
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Zhang, Y., Tomuro, N., Furst, J., Stan Raicu, D. (2010). Using BI-RADS Descriptors and Ensemble Learning for Classifying Masses in Mammograms. In: Caputo, B., Müller, H., Syeda-Mahmood, T., Duncan, J.S., Wang, F., Kalpathy-Cramer, J. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2009. Lecture Notes in Computer Science, vol 5853. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11769-5_7
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DOI: https://doi.org/10.1007/978-3-642-11769-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11768-8
Online ISBN: 978-3-642-11769-5
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