Classification of mammographic masses using support vector machines and Bayesian networks

Maurice Samulski; Nico Karssemeijer; Peter Lucas M.D.; Perry Groot

doi:10.1117/12.709679

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30 March 2007 Classification of mammographic masses using support vector machines and Bayesian networks

Maurice Samulski, Nico Karssemeijer, Peter Lucas M.D., Perry Groot

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Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65141J (2007) https://doi.org/10.1117/12.709679
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

In this paper, we compare two state-of-the-art classification techniques characterizing masses as either benign or malignant, using a dataset consisting of 271 cases (131 benign and 140 malignant), containing both a MLO and CC view. For suspect regions in a digitized mammogram, 12 out of 81 calculated image features have been selected for investigating the classification accuracy of support vector machines (SVMs) and Bayesian networks (BNs). Additional techniques for improving their performance were included in their comparison: the Manly transformation for achieving a normal distribution of image features and principal component analysis (PCA) for reducing our high-dimensional data. The performance of the classifiers were evaluated with Receiver Operating Characteristics (ROC) analysis. The classifiers were trained and tested using a k-fold cross-validation test method (k=10). It was found that the area under the ROC curve (A_z) of the BN increased significantly (p=0.0002) using the Manly transformation, from A_z = 0.767 to A_z = 0.795. The Manly transformation did not result in a significant change for SVMs. Also the difference between SVMs and BNs using the transformed dataset was not statistically significant (p=0.78). Applying PCA resulted in an improvement in classification accuracy of the naive Bayesian classifier, from A_z = 0.767 to A_z = 0.786. The difference in classification performance between BNs and SVMs after applying PCA was small and not statistically significant (p=0.11).

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Maurice Samulski, Nico Karssemeijer, Peter Lucas M.D., and Perry Groot "Classification of mammographic masses using support vector machines and Bayesian networks", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65141J (30 March 2007); https://doi.org/10.1117/12.709679

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