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In response to this issue, a multi-scale and lightweight U-Net lung image segmentation algorithm with an attention mechanism is proposed. This algorithm introduces CA convolution after the convolution in the encoding stage to extract channel relationships and positional information from the feature maps. Furthermore, the RFB module is employed to extract features from different perspectives. Lastly, upward residual connections are introduced between the RFB modules in the encoder and decoder to enhance inter-network information interaction. Experiments conducted on the LUNA (lung nodule analysis) dataset and the COVID-QU-Ex dataset for COVID-19 pneumonia demonstrate that the proposed MSA-UNet algorithm achieves the best results in terms of Precision and Dice metrics. It outperforms mainstream models such as U-Net++ and DeeplabV3+ in terms of segmentation effectiveness and segmentation generality. The model has a floating-point operation count (FLOPs) of 18.15 G, a network parameter counts of 8.83\u00d7106, and achieves a Precision of 99.37%. The algorithm achieves a good balance between computational efficiency, model size, and segmentation accuracy. <\/jats:p>","DOI":"10.1142\/s0218213023500690","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T09:14:44Z","timestamp":1695892484000},"source":"Crossref","is-referenced-by-count":2,"title":["MSA-UNet: A Multiscale Lightweight U-Net Lung CT Image Segmentation Algorithm Under Attention Mechanism"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4025-094X","authenticated-orcid":false,"given":"Chuantao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, No. 15, Yongyuan Road, Huangcun Town, Daxing District, Beijing 102616, China"},{"name":"Beijing Building Safety Monitoring Engineering Technology Research Center, Beijing 102616, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6204-4460","authenticated-orcid":false,"given":"Shuo","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, No. 15, Yongyuan Road, Huangcun Town, Daxing District, Beijing 102616, China"}]},{"given":"Jiajun","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Street, Zhongshan District, Dalian 116001, Liaoning, China"}]},{"given":"Xiumin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, No. 15, Yongyuan Road, Huangcun Town, Daxing District, Beijing 102616, China"}]},{"given":"Baoxia","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, No. 15, Yongyuan Road, Huangcun Town, Daxing District, Beijing 102616, China"}]}],"member":"219","published-online":{"date-parts":[[2024,3,30]]},"reference":[{"key":"S0218213023500690BIB001","doi-asserted-by":"publisher","DOI":"10.1378\/chest.06-2663"},{"issue":"3","key":"S0218213023500690BIB002","first-page":"332","volume":"40","author":"Chu P.","year":"2017","journal-title":"Journal of Hefei University of Technology"},{"key":"S0218213023500690BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"S0218213023500690BIB004","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"S0218213023500690BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.01.104"},{"key":"S0218213023500690BIB006","first-page":"3","volume-title":"4th Int. 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