Abstract: In this paper,a novel deep learning model,LST-ARBunet,is proposed to solve the problem of accurate segmentation of lung nodules in lung computed tomography(CT) images.In the field of lung nodule detection,it is difficult to realize the technology due to factors such as tiny nodule size,diverse morphology and high similarity with surrounding tissues.The main innovations of the LST-ARBunet model are the incorporation of the Swin-Transformer structure in the downsampling process to capture the features of the lung images in different scales;the Swin-Transformer structure is subjected to a local convolutional fronts and shared parameter processing to reduce the number of model parameters;incorporating a customized attention mechanism in the upsampling process to capture important detailed features;and using inverted residual blocks instead of normal convolution to lighten the model.Experimental validation on the publicly available lung nodule CT dataset LIDC-IDRI,LST-ARBunet demonstrates some performance improvement,with an intersection over union(IoU) of 0.889 and average symmetric surface distance(ASSD) of 1.453,and Dice similarity score(Dice Score) of 0.884,all of which outperform the models of the ablation experiments as well as the ResUnet,PSPNet,and DeepLabv3+ models.In addition,LST-ARBunet maintains a high segmentation accuracy while maintaining a relatively reasonable inference time of 1.3s,providing a feasible balance of efficiency for clinical applications.This study provides a new technical approach to lung nodule segmentation,and future work will explore the model’s performance on more diverse clinical datasets,further optimize the model efficiency,and advance its deployment and application in real-world healthcare environments to provide strong support for the early detection and treatment of lung cancer.

Key words: U-net, Swin-Transformer, Residual bottleneck lightening, Attention mechanism, Lung nodule segmentation