Ultrasonographic feature selection and pattern classification for cervical lymph nodes using support vector machines

A rough margin based support vector machine (RMSVM) classifier was proposed to improve the accuracy of ultrasound diagnoses for cervical lymph nodes. Thirty-six features belonging to 10 kinds of ultrasonographic characteristics were extracted for each of 110 lymph nodes in ultrasonograms. Comparison studies were done for three classifiers—the classical support vector machine (SVM), the general regression neural network and the proposed RMSVM, with or without the feature selection by the recursive feature elimination (RFE) algorithm, respectively, based on SVMs and the mean square error discriminant. It was indicated by experimental results that all classifiers benefited from the feature selection. The best classification performance was obtained by the RMSVM using thirteen features selected by the RMSVM based RFE, which yielded the normalized area under the receiver operating characteristic curve (Az) of 0.859. Compared with the radiologist's performance of Az of 0.787, the developed computer-aided diagnosis algorithm has the potential to improve the diagnostic accuracy.