{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:31:51Z","timestamp":1765233111026,"version":"3.41.2"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T00:00:00Z","timestamp":1622678400000},"content-version":"vor","delay-in-days":153,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005270","name":"Fujian Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["2020HZ02014"],"award-info":[{"award-number":["2020HZ02014"]}],"id":[{"id":"10.13039\/501100005270","id-type":"DOI","asserted-by":"publisher"}]}],"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>Ultrasound is one of the critical methods for diagnosis and treatment in thyroid examination. In clinical application, many reasons, such as large outpatient traffic, time\u2010consuming training of sonographers, and uneven professional level of physicians, often cause irregularities during the ultrasonic examination, leading to misdiagnosis or missed diagnosis. In order to standardize the thyroid ultrasound examination process, this paper proposes using a deep learning method based on residual network to recognize the Thyroid Ultrasound Standard Plane (TUSP). At first, referring to multiple relevant guidelines, eight TUSP were determined with the advice of clinical ultrasound experts. A total of 5,500 TUSP images of 8 categories were collected with the approval and review of the Ethics Committee and the patient\u2019s informed consent. Then, after desensitizing and filling the images, the 18\u2010layer residual network model (ResNet\u201018) was trained for TUSP image recognition, and five\u2010fold cross\u2010validation was performed. Finally, through indicators like accuracy rate, we compared the recognition effect of other mainstream deep convolutional neural network models. Experimental results showed that ResNet\u201018 has the best recognition effect on TUSP images with an average accuracy rate of 91.07%. The average macro precision, average macro recall, and average macro F1\u2010score are 91.39%, 91.34%, and 91.30%, respectively. It proves that the deep learning method based on residual network can effectively recognize TUSP images, which is expected to standardize clinical thyroid ultrasound examination and reduce misdiagnosis and missed diagnosis.<\/jats:p>","DOI":"10.1155\/2021\/5598001","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T19:54:58Z","timestamp":1622750098000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network"],"prefix":"10.1155","volume":"2021","author":[{"given":"Minghui","family":"Guo","sequence":"first","affiliation":[]},{"given":"Kangjian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shunlan","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4261-9648","authenticated-orcid":false,"given":"Yongzhao","family":"Du","sequence":"additional","affiliation":[]},{"given":"Peizhong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qichen","family":"Su","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3123-1138","authenticated-orcid":false,"given":"Guorong","family":"Lv","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/bmb\/ldr030"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"volume-title":"Thyroid Nodule: Current Evaluation and Management, in the Thyroid and its Diseases","year":"2019","author":"Parsa A. 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