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Computational Intelligence and Neuroscience
Volume 2019, Article ID 7401235, 13 pages
Research Article

Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT

1School of Computer Science and Technology & Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, China University of Mining and Technology, Xuzhou 221116, China
2School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
3Department of Computed Tomography, Xuzhou Third People’s Hospital, Xuzhou 221116, China

Correspondence should be addressed to Yanjun Hu; nc.ude.tmuc@uhjy

Received 2 July 2019; Revised 29 August 2019; Accepted 4 September 2019; Published 20 October 2019

Academic Editor: Juan Carlos Fernández

Copyright © 2019 Zuopeng Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of computed tomography (CT), the contrast-enhanced CT scan is widely used in the diagnosis of thyroid nodules. 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.