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Breast cancer is considered the most commonly diagnosed cancer globally and falls second to lung cancer. For the early detection of breast tumors in women, breast cancer analysis using Ultrasound, Mammography, and MRI modalities as the initial screening process. Due to the random variation, irregular shapes, and blurred boundaries of tumor regions, the accurate segmentation of breast tumors is still a tricky task. The existing convolutional neural networks (CNNs) inherit their limitation by extracting global context information and, in most cases, proved less efficient in obtaining satisfactory results. As a solution, we proposed the BTS-ST network, a novel solution for breast tumor segmentation and classification that Swin-Transformer (ST) inspires. The BTS-ST network incorporates Swin-Transformer into traditional CNNs-based U-Net to improve global modeling capabilities. To improve the feature representation …