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To augment the classification accuracy of the ultrasound computer-aided diagnosis (CAD) for breast tumor detection based on texture feature, we proposed to extract texture feature descriptors by the shearlet transform. Shearlet transform provides a sparse representation of high dimensional data with especially superior directional sensitivity at various scales. Therefore, shearlet-based texture feature descriptors can characterize breast tumors well. In order to objectively evaluate the performance of shearlet-based features, curvelet, contourlet, wavelet and gray level co-occurrence matrix based texture feature descriptors are also extracted for comparison. All these features were then fed to two different classifiers, support machine vector (SVM) and AdaBoost, to evaluate the consistency. The experimental results of breast tumor classification showed that the classification accuracy, sensitivity, specificity, positive …