{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:55:15Z","timestamp":1781016915265,"version":"3.54.1"},"reference-count":35,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Scientific Research Project of Shaanxi Province","award":["2021B004"],"award-info":[{"award-number":["2021B004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2022,3,23]]},"abstract":"<jats:p>Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors\u2019 work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors\u2019 diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor\u2019s diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.<\/jats:p>","DOI":"10.1155\/2022\/5112867","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:05:32Z","timestamp":1648080332000},"page":"1-11","source":"Crossref","is-referenced-by-count":6,"title":["Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8808-1102","authenticated-orcid":true,"given":"Na","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6193-7460","authenticated-orcid":true,"given":"Jianping","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3807-4094","authenticated-orcid":true,"given":"Bengang","family":"Hui","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7705-6174","authenticated-orcid":true,"given":"Bowei","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5079-6110","authenticated-orcid":true,"given":"Weibo","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0040-6620","authenticated-orcid":true,"given":"Rongxin","family":"Shang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0544-7197","authenticated-orcid":true,"given":"Xiaoping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9971-004X","authenticated-orcid":true,"given":"Jie","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1038\/s41392-020-00243-2"},{"key":"2","first-page":"1509","article-title":"Lungs segmentation using multi-level thresholding in CT images","volume":"1","author":"A. 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