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However, diagnosis of the thyroid nodules is labor and time intensive for radiologists and strongly depends on the personal experience of the radiologists. In this pursuit, the present study envisaged to develop a deep learning\u2010based computer\u2010aided diagnosis (CAD) method that enabled the automatic detection and classification of suspicious thyroid nodules in order to reduce the unnecessary fine\u2010needle aspiration biopsy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The CAD method consisted of two main parts: detecting the location of thyroid nodules using a multiscale detection network and classifying the detected thyroid nodules by an attention\u2010based classification network.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The performance of the proposed method was evaluated and compared with that of other state\u2010of\u2010the\u2010art deep learning methods and experienced radiologists. The proposed detection method outperformed three other detection architectures (average precision, 82.1%\u00a0vs. 78.3%, 77.2%, and 74.8%). Moreover, the classification method showed a superior performance compared with four other state\u2010of\u2010the\u2010art classification networks (accuracy, 94.8%\u00a0vs. 91.2%, 85.0%, 80.8%, and 72.1%) and that by experienced radiologists (mean value of area under the curve, 0.941\u00a0vs. 0.833).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study verified the high efficiency of the proposed detection method. The findings can help improve the diagnostic performance of radiologists. However, the developed CAD system requires more training and evaluation in a large\u2010population study.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15319","type":"journal-article","created":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T11:34:33Z","timestamp":1635680073000},"page":"7959-7970","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A deep learning\u2010based method for detecting and classifying the ultrasound images of suspicious thyroid nodules"],"prefix":"10.1002","volume":"48","author":[{"given":"Zijian","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering Shandong University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congmin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering Shandong University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong First Medical University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong First Medical University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong First Medical University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong First Medical University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101665"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2008-1724"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22363"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0176103"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/jum.14316"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2009.04.001"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.7863\/jum.2011.30.9.1259"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2011.10.001"},{"key":"e_1_2_9_10_1","volume-title":"Advances in Intelligent Systems and Computing","author":"Koundal D","year":"2013"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.7863\/ultra.14.09057"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_13_1","unstructured":"SimonyanK ZissermanA. 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