Abstract: Ultrasound imaging is the most commonly used technology for breast lesion detection and automated lesion detection of breast ultrasound images has attracted increasing attention from researchers.However,most studies fail to fully integrate image information to enhance features,and they do not take into account the problems of the increased model complexity and neglect the issue of rising false positive rates.Therefore,this paper improves existing RetinaNet model by using VMamba as the backbone network and proposes a lesion detection network based on dual attention mechanism and classification supervision(DACSNet) to improve the accuracy of lesion detection in breast ultrasound images and reduce the false-positive rate.Specifically,this paper incorporates medical domain knowledge into the attention module,where the dual attention module(DAM) effectively enhances feature representation in both the channel and spatial dimensions.The DAM involves only a small number of parameters and effectively boosts the model’s detection performance.Furthermore,to reduce the false-positive rate of lesion detection,a classification supervision module(CSM) is added the model to integrate lesion classification information,achieving secondary focus on suspected lesion areas.To verify the performance of DACSNet,experiments for breast lesion detection are conducted on three sets of publicly available breast ultrasound image datasets,and the experimental results demonstrate the effectiveness of this method.

Key words: Breast ultrasound image, Lesion detection, VMamba, Dual attention module, Classification supervision