The most common type of cancer among women worldwide is breast cancer. Early detection of breast cancer is very important to reduce the fatality rate. For the hundreds of mammographic images scanned by a radiologist, only a few are cancerous. While detecting abnormalities, some of them may be missed, as the detection of suspicious and abnormal images is a recurrent mission that causes fatigue and eyestrain. In this paper, a computer-aided diagnosis system using the curvelet transform (CT) algorithm is proposed for interpreting mammograms to improve the decision making. The purpose of this study is to develop a method for the characterization of the mammography as both normal and abnormal regions, and to determine its diagnostic performance to differentiate between malignant and benign ones. The multiresolution CT that was recently derived is used to differentiate among 200 mammograms: 50 malignant, 50 benign, and 100 normal. A support vector machine and the k-nearest neighbor algorithm are used as classifiers to build the diagnostic model and are also used for the principal component analysis and linear discriminant analysis for further dimensional reduction and feature selection. A dataset from the Mammographic Image Analysis Society database is used for testing the method.