{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:26:32Z","timestamp":1775064392766,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"vor","delay-in-days":146,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Key Research and Development Project","award":["2019C03088"],"award-info":[{"award-number":["2019C03088"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Thyroid nodule lesions are one of the most common lesions of the thyroid; the incidence rate has been the highest in the past thirty years. X\u2010ray computed tomography (CT) plays an increasingly important role in the diagnosis of thyroid diseases. Nonetheless, as a result of the artifact and high complexity of thyroid CT image, the traditional machine learning method cannot be applied to CT image processing. In this paper, an end\u2010to\u2010end thyroid nodule automatic recognition and classification system is designed based on CNN. An improved Eff\u2010Unet segmentation network is used to segment thyroid nodules as ROI. The image processing algorithm optimizes the ROI region and divides the nodules. A low\u2010level and high\u2010level feature fusion classification network CNN\u2010F is proposed to classify the benign and malignant nodules. After each module is connected in series with the algorithm, the automatic classification of each nodule can be realized. Experimental results demonstrate that the proposed end\u2010to\u2010end thyroid nodule automatic recognition and classification system has excellent performance in diagnosing thyroid diseases. In the test set, the segmentation IOU reaches 0.855, and the classification output accuracy reaches 85.92%.<\/jats:p>","DOI":"10.1155\/2021\/5540186","type":"journal-article","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:02:27Z","timestamp":1622160147000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Automatic Recognition and Classification System of Thyroid Nodules in CT Images Based on CNN"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-3256","authenticated-orcid":false,"given":"Wenjun","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2822-6653","authenticated-orcid":false,"given":"Siyi","family":"Cheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0593-9589","authenticated-orcid":false,"given":"Kai","family":"Qian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0880-9798","authenticated-orcid":false,"given":"Keqiang","family":"Yue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4700-7755","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.otc.2010.01.002"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2014.1"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"NugrohoH. 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