Abstract: To address the problems of low contrast, fuzzy texture details, and inadequate edge feature extraction in lung lesion images, this paper proposed a novel lightweight U-shaped network incorporating dual attention, termed DALU-Net. First, an attentionbased two-branch fusion module was designed. During encoding, the two branches focused on global and local information, respectively, to capture global localization information and lesion edge features. Then, a parallel texture enhancement module was introduced,and a statistical feature histogram was obtained using a quantization counting operator to enhance the texture features extracted by the shallow network and address the challenge of low contrast. Finally, a reverse-attention dual-interference refinement module was developed to enable the network to focus on and process mis-segmentation information during the decoding stage, thus simultaneously eliminating false-positive and false-negative features in the reconstructed image. The effectiveness of the network was verified on two lung lesion datasets: COVID-19 CT scan and MS COVID-19. Compared with existing networks,the proposed network achieves the best results on all five evaluation metrics, with a Dice score that is 1.42% higher than that of the second-best model, UNeXt, while also using fewer parameters.

Key words: lung lesion image, lightweight U-shaped network, dual attention mechanism, texture enhancement