A new support vector machine method for medical image classification

One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere and the normal data, and the exterior margin between this surface and the abnormal data are as large as possible. The proposed method is easily implemented and can reduce both false positive and false negative error rates to obtain very good classification results. Experiments were performed on three medical image data sets to evaluate the proposed method.