Boosting Twin Support Vector Machine Approach for MCs Detection

Clustered microcalcifications (MCs) are one of the early signs of breast cancer, and they are of great importance for an early diagnosis. Moreover, the spatial distribution and the shape of the microcalcifications have a significant impact in medical practice to evaluate the probability of malignancy of the tumor. In this paper we investigate an approach based on boosted twin support vector (Boosting-TWSVM) for detection of microcalcifications clusters (MCs) in digital mammograms.In the algorithm, we formulate MCs detection as a supervised-learning problem and apply the trained Boosted-TWSVM classifier to develop the detection algorithm. We tested the proposed method using DDSM database of 80cases mammograms containing about 980 MCs. Detection performance of the proposed method is evaluated by using receiver operating characteristic (ROC) curves. We compared the proposed algorithm with other existing methods. In our experiments, the proposed detection method outperformed the other methods tested. In particular, a sensitivity as high as 92.35% was achieved by our detection algorithm at an error rate of 8.3%. The experiment results suggest that Boosted-TWSVM is a promising technique for MCs detection.