ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging

Classification of microcalcification clusters from mammograms plays essential roles in computer-aided diagnosis for early detection of breast cancer, where support vector machine (SVM) and artificial neural network (ANN) are two commonly used techniques. Although some work suggest that SVM performs better than ANN, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve Az. This performance may become much worse when the training samples are imbalanced. As a result, a new strategy namely balanced learning with optimized decision making is proposed to enable effective learning from imbalanced samples, which is further employed to evaluate the performance of ANN and SVM in this context. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from both ANN and SVM has been significantly improved. Although ANN outperforms SVM when balanced learning is absent, the performance from the two classifiers becomes very comparable when both balanced learning and optimized decision making are employed. Consequently, an average improvement of more than 10% in the measurements of F ₁ score and Az measurement are achieved for the two classifiers. This has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.