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However, due to the artifacts and high complexity of thyroid CT images, traditional machine learning has difficulty in detecting thyroid nodules in contrast-enhanced CT. A fully automated detection algorithm for thyroid nodules using contrast-enhanced CT images is developed. A modified U-Net architecture of fully convolutional networks is employed to segment the thyroid region of interest (ROI), and a fusion of convolutional neural networks (CNN-Fs) is proposed to detect benign and malignant thyroid nodules from the ROI images and original contrast-enhanced CT images. Experimental results demonstrate that the proposed cascade and fusion method of multitask convolutional neural networks (CNNs) is efficient in diagnosing thyroid diseases with contrast-enhanced CT images and has superior performance compared with other CNN methods.<\/jats:p>","DOI":"10.1155\/2019\/7401235","type":"journal-article","created":{"date-parts":[[2019,10,20]],"date-time":"2019-10-20T19:31:38Z","timestamp":1571599898000},"page":"1-13","source":"Crossref","is-referenced-by-count":21,"title":["Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT"],"prefix":"10.1155","volume":"2019","author":[{"given":"Zuopeng","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology & Mine Digitization Engineering Research Center of Ministry of Education of the People\u2019s Republic of China, China University of Mining and Technology, Xuzhou 221116, 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