Computer-aided diagnosis of breast cancer from magnetic resonance imaging examinations by custom radial basis function vector machine

This paper presents a new method for performing supervised learning (classification) and demonstrates the technique by applying it to the detection of breast cancer from the dynamic information obtained in magnetic resonance imaging examinations. The proposed method is a vector machine similar to the established support vector machine (SVM) method, however, our method involves a reformulation of the classification/prediction process. The proposed classification methodology is compared with the SVM, with both methods using the established radial basis function kernel. The proposed vector machine formulation applies test biasing in a new manner and is demonstrated to produce robust solutions as measured by the receiver operating characteristic (ROC) curve area. The technique is compared with SVMs and yields test improvements up to an additional 9.8% sensitivity or 7.2% specificity.