Breast cancer is among the leading causes of mortality in women worldwide. Early detection can increase the survival rate and limit cancer metastasis to other organs. Recently, the role of ultrasound (US) imaging in the diagnosis and monitoring of breast tumors besides X-ray mammography has been increasing. Several computer-aided diagnosis (CAD) systems were proposed to improve the classification of breast tumors. This work presents a fast and computationally-efficient technique to distinguish between malignant and benign breast tumors. The technique applies wavelet packet transform (WPT) on conventional brightness mode (B-mode) US images, and then extracts several textural and morphological features from the approximation decomposition part. Features include first-order statistics (FOS), fractal dimension texture analysis (FDTA), spatial gray-level dependence matrices (SGLDM), area, perimeter, and compactness of the lesion. When the support vector machine was applied for classification on original US images, the classifier exhibited 97.4% accuracy, 98.3% sensitivity, and 92.1% specificity. These performance parameters were slightly changed to 96.9% accuracy, 96.7% sensitivity, and 97% specificity, when the same features were extracted from WPT. However, the frame classification time was reduced drastically from 1.1284s using original US images to 0.0604s after incorporating WPT. Hence, the proposed CAD system using WPT was able to decrease the computational complexity and processing time by, at least, eight times. This shall improve the early detection of breast cancer via developing real-time and noninvasive computer-aided diagnostic software.