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Breast cancer is the second leading cause of cancer-related death in women. Ultrasound imaging has been widely used for the early detection of breast cancer because of its superior ability in imaging dense breast tissue and its lack of ionizing radiation. However, ultrasound imaging heavily depends on practitioners’ experience and thus becomes relatively subjective. In this work, we proposed a novel multi-scale view-based convolutional neural network (MSVCNN) to assist doctors to diagnose and improve classification accuracy. MSV-CNN takes full images, regions of interest (ROI), and the tumor regions with two times size of the ROI as input. It adopts three complementary branches to learn multi-scale view features from different views. The sub-networks in all branches have the same structure but with different parameters. The output of three branches is finally concatenated and fused by a fully connected layer …