A computer-aided diagnosis (CAD) system has
been examined to reduce the effort of radiologist. In the
mammogram, it is helpful to improve the diagnostic accuracy
of malignancy microcalcifications in early stage of detecting
breast cancer. In this paper, we propose a microcalcification
detection method using multi-layer support vector machine
(SVM) classifiers to determine whether microcalcifications are
malignant or benign tumors. The proposed microcalcification
detection is divided into two steps, each of which uses a SVM
classifier. First, potential ROIs (Region of interest) those are
suspicious as malignant tumors are detected as a coarse detection
level. And then, each ROI is classified whether it is malignant
or not. The proposed algorithm is applied to the Korean
digital mammogram. Experimental result showed that the
proposed method would outperform conventional method
using ANN (artificial neural networks).