Mammographic mass detection by vicinal support vector machine

We proposed a Vicinal Support Vector Machine (VSVM) as an enhancement learning algorithm for mammographic mass detection on digital mammograms. The detection scheme includes two steps. First, one-class Support Vector Machine (SVM) is applied for the abnormal cases detection, where only normal cases are served as training samples. Then VSVM is investigated for the malignant cases detection. The aim of this step is to decide whether a detected abnormal case is benign or malignant. For the proposed VSVM algorithm, the whole training data are clustered into different soft vicinal areas in feature space by kernel based deterministic annealing (KBDA) method. The choice of different number of clusters makes VSVM be adaptive to different data structures in the input space. We tested the proposed scheme by using 90 clinical mammograms from MIAS database. The corresponding accuracy was observed to be 84%, with an area of A/sub z/=0.89 under the receiver operating characteristics (ROC) curve. The experimental results show that the two-step detection scheme works effective and the proposed VSVM is a promising classifier for breast mass detection.