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Accurate breast lesion segmentation is a great help in the initial stage of breast cancer treatment planning. Ultrasound is considered the safe and cheapest method for the breast screening process. However, ultrasound images inherently contain speckle noise, unclear boundaries, and complex shapes, making it more challenging for automatic segmentation methods. This work proposes a multiscale dual attention-based network (MDA-Net) for concurrent segmentation of breast lesions images. The multiscale fusion (MF) block is introduced that addresses the classical fixed receptive field issues, and helps to extract more semantic features and aims to achieve more features diversity. A dual-attention (dA) is also proposed, which is a hybrid of channel-based attention (cA) and lesion attention (lA) blocks that improves the feature representation capability and adaptatively learns a discriminative representation of high …