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Articles

Computer-Aided Detection and Diagnosis of Thyroid Nodules Using Machine and Deep Learning Classification Algorithms

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References

  • H. Kim, C. M. Park, S. M. Lee, H. J. Lee, and J. M. Goo, “A comparison of two commercial volumetry software programs in the analysis of pulmonary ground-glass nodules: segmentation capability and measurement accuracy,” Korean J. Radiol., Vol. 14, pp. 683–91, 2013.
  • U. R. Acharya, S. V. Sree, M. M. Krishnan, F. Molinari, W. Zieleźnik, R. H. Bardales, et al., “Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population,” J Ultrasound Med., Vol. 33, pp. 245–53, 2014.
  • Y. Wang, K. R. Lei, Y. P. He, X. L. Li, W. W. Ren, C. K. Zhao, et al., “Malignancy risk stratification of thyroid nodules: comparisons of four ultrasound thyroid Imaging Reporting and Data Systems in surgically resected nodules,” Sci. Rep., Vol. 7, pp. 1–10, 2017.
  • Y. J. Yoo, E. J. Ha, Y. J. Cho, H. L. Kim, M. Han, and S. Y. Kang, “Computer-aided diagnosis of thyroid nodules via ultrasonography: initial clinical experience,” Korean J Radiol., Vol. 19, pp. 665–72, 2018.
  • E. G. Grant, F. N. Tessler, J. K. Hoang, J. E. Langer, M. D. Beland, L. L. Berland, J. J. Cronan, and T. S. Desser, “Thyroid ultrasound reporting lexicon: white paper of the ACR Thyroid Imaging,” Reporting and Data System (TIRADS) Committee, J. Am. Coll. Radiol, Vol. 12, no. 12, pp. 1272–9, 2017.
  • D. Koundal, S. Gupta, and S. Singh, “Computer-Aided diagnosis of Thyroid Nodule: A review,” International Journal of Computer Science & Engineering Survey (IJCSES), Vol. 3, no. 4, pp. 67–83, 2012.
  • A. Sarti, C. Corsi, E. Mazzini, and C. Lamberti, “Maximum likelihood segmentation of ultrasound images with Rayleigh distribution,” IEEE Trans. Ultrason. Ferroelect. Freq. Control, Vol. 52, no. 6, pp. 947–60, 2005.
  • E. G. Keramidas, D. K. Iakovidis, D. Maroulis, and N. Dimitropoulos, “Thyroid Texture representation via noise resistant image features,” IEEE Int. Symp. on Computer-Based Medical Systems, Vol. 1, pp. 560–5, 2008.
  • L. Wang, S. Yang, S. Yang, et al., “Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network,” World J Surg Onc., Vol. 17, no. 12, pp. 1–12, 2019.
  • J. Ma, F. Wu, J. Zhu, D. Xu, and D. Kong, “A pre-trained convolutional neural network based method for thyroid nodule diagnosis,” Ultrasonics, Vol. 73, pp. 221–30, 2017.
  • T. Liu, S. Xie, J. Xu, L. Niu, and W. Sun. “Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features,” Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 919–23, New Orleans, LA, USA, 2017.
  • E. Dogantekin, A. Dogantekin, and D. Avci, “An expert system based on generalized discriminant analysis and wavelet support vector machine for diagnosis of thyroid diseases,” Expert. Syst. Appl., Vol. 38, no. 1, pp. 146–50, 2011.
  • H.-L. Chen, B. Yang, G. Wang, J. Liu, Y.-D. Chen, and D.-Y. Liu, “A three-stage expert system based on support vector machines for thyroid disease diagnosis,” J. Med. Syst., Vol. 36, no. 3, pp. 1953–63, 2011.
  • K. Nakamura, H. Yoshida, R. Engelmann, H. MacMahon, S. Katsuragawa, T. Ishida, et al., “Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks,” Radiology, Vol. 214, pp. 823–30, 2000.
  • J. Ding, H. D. Cheng, J. Huang, and Y. Zhang. “Multiple-instance learning with global and local features for thyroid ultrasound image classification”, 7th International Conference on Biomedical Engineering and Informatics, pp. 66–70, 2014.
  • K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for imaging recognition,” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 770–8, 2016, pp. 770–8.
  • A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artificial Intell. Rev., Vol. 53, pp. 5455–516, 2020.
  • J. Chi, E. Walia, P. Babyn, et al., “Thyroid Nodul classification in ultrasound images by fine-tuning deep convolutional neural network,” J. Digit Imaging, Vol. 30, pp. 477–86, 2017.
  • M. A. Savelonas, D. K. Iakovidis, I. Legakis, and D. Maroulis, “Active contours guided by echogenicity and texture for delineation of thyroid nodules in ultrasound images,” IEEE Trans. Inf. Technol. Biomed., Vol. 13, no. 4, pp. 519–27, 2009.
  • https://opencas.webarchiv.kit.edu/?q=node/29
  • M. Sokouti, M. Sokouti, and B. Sokouti, “Computer aided diagnosis of Thyroid cancer using image processing Techniques,” International Journal of Computer Science and Network Security, Vol. 18, no. 4, pp. 1–8, 2018.
  • J. Xia, H. Chen, Q. Li, M. Zhou, L. Chen, Z. Cai, Y. Fang, and H. Zhou, “Ultrasound-based differentiation of malignant and benign thyroid nodules: An extreme learning machine approach,” Comput. Methods Programs Biomed., Vol. 147, no. 1, pp. 37–49, 2017.

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