{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T14:40:33Z","timestamp":1777560033434,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIC"],"published-print":{"date-parts":[[2023,10,13]]},"abstract":"<jats:p>Lung cancer is the leading cause of cancer death worldwide, and most patients are diagnosed with advanced stages for lack of symptoms in the early stages of the disease, leading to poor prognosis. It is thus of great importance to detect lung cancer in the early stages which can reduce mortality and improve patient survival significantly. Although there are many computer aided diagnosis (CAD) systems used for detecting pulmonary nodules, there are still few CAD systems for detection and segmentation, and their performance on small nodules is not ideal. Thus, in this paper, we propose a deep cascaded multitask framework called mobilenet split-attention Yolo unet, the mobilenet split-attention Yolo(Msa-yolo) greatly enhance the feature of small nodules and boost up their performance, the overall result shows that the mean accuracy precision (mAP) of our Msa-Yolo compared to Yolox has increased from 85.10% to 86.64% on LUNA16 dataset, and from 90.13% to 94.15% on LCS dataset compared to YoloX. Besides, we get only 8.35 average number of candidates per scan with 96.32% sensitivity on LUNA16 dataset, which greatly outperforms other existing systems. At the segmentation stage, the mean intersection over union (mIOU) of our CAD system has increased from 71.66% to 76.84% on LCS dataset comparing to baseline. Conclusion: A fast, accurate and robust CAD system for nodule detection, segmentation and classification is proposed in this paper. And it is confirmed by the experimental results that the proposed system possesses the ability to detect and segment small nodules.<\/jats:p>","DOI":"10.3233\/aic-220318","type":"journal-article","created":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T15:04:51Z","timestamp":1691939091000},"page":"269-284","source":"Crossref","is-referenced-by-count":2,"title":["Fully Automated Neural Network Framework for Pulmonary Nodules Detection and Segmentation"],"prefix":"10.1177","volume":"36","author":[{"given":"Yixin","family":"Xiong","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Automation, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongcheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Automation, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujuan","family":"Wang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Automation, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanxing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, UniArmy Medical Versity Xinqiao Hospital, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Automation, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/AIC-220318_ref1","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans","volume":"38","author":"Armato","year":"2011","journal-title":"Medical physics"},{"key":"10.3233\/AIC-220318_ref2","doi-asserted-by":"publisher","DOI":"10.1098\/rsob.170070"},{"key":"10.3233\/AIC-220318_ref4","doi-asserted-by":"publisher","first-page":"154007","DOI":"10.1109\/ACCESS.2020.3018666","article-title":"A comprehensive review of computer-aided diagnosis of pulmonary nodules based on computed tomography scans","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"issue":"7","key":"10.3233\/AIC-220318_ref8","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","article-title":"Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection","volume":"64","author":"Dou","year":"2016","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"10.3233\/AIC-220318_ref10","unstructured":"Z. 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