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Deep learning-based breast lesion detection in ultrasound images has demonstrated great potential to provide objective suggestions for radiologists and improve their accuracy in diagnosing breast diseases. However, the lack of an effective feature enhancement approach limits the performance of deep learning models. Therefore, in this study, we propose a novel dual global attention neural network (DGANet) to improve the accuracy of breast lesion detection in ultrasound images. Specifically, we designed a bilateral spatial attention module and a global channel attention module to enhance features in spatial and channel dimensions, respectively. The bilateral spatial attention module enhances features by capturing supporting information in regions neighboring breast lesions and reducing integration of noise signal. The global channel attention module enhances features of important channels by weighted …