A multiscale cascaded attention network(MCANet)is proposed to solve the problems of low utilization rate of global context information,low segmentation accuracy and fuzzy segmentation details of different scale features in lung parenchymal segmentation task.The network is mainly composed of a multi-scale feature extraction network (MSFENet),a multi-scale attention guidance(MSAG)module,and a decoding feature integrator(DFI).Firstly,the MSFENet network is designed to improve the spatial interaction ability of feature information in different channel dimensions,retain the key features of the image to the greatest extent in the sampling process,and enrich the global context information. Then,MSAG is designed to improve the utilization of multi-scale feature information in the decoding process of the model,and maximize the integration of the advantages of the two attention mechanisms.Finally, DFI was designed to reintegrate the decoding features generated by the decoder to improve the segmentation performance of the model for edge information.In this paper,the performance of the model was verified by experiments on the LUNA16 dataset,and the Dice of 0.993 and HD of 3.864 were obtained,and the experimental results proved that MCANet has better segmentation performance and can segment the lung parenchyma more accurately than other mainstream medical segmentation models.