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Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

A Study on Thyroid Nodule Image Classification System Using Small Amount of Training Samples

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DOI: http://dx.doi.org/10.15439/2022R12

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 129–133 ()

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Abstract. To reduce errors caused by traditional diagnostic methods that rely heavily on physician experience, the diagnostic systems based on computer-aided have been researched and developed to assist physicians in diagnosing thyroid disease. Therefore, performance of the computer systems plays an important role to improve the quality of diagnostic processes. Although there has been a number of publish related to this issue, those studies still have limitations in which needing large data sets for training classification models is considered the most concerning limitation of previous studies. To solve this limitations, in this work, a classification method using artificial intelligence with a small amount of data to analyze thyroid ultrasound images was proposed. Thus we can save time and effort for data collection and the classification model processing time. Through baseline tests with an open data set, especially thyroid digital image database (TDID), the proposed method has improved the limitations of previous methods.

References

  1. TDID dataset Pedraza, L.; Vargas, C.; Narvaez, F.; Duran, O.; Munoz, E.; Romero, E. An open access thyroid ultrasoundimage database. In Proceedings of the 10th International Symposium on Medical Information Processing and Analysis, Cartagena de Indias, Colombia, 28 January 2015; pp. 1–6.
  2. Koundal, D.; Gupta, S.; Signh, S. Computer aided thyroid nodule detection system using medical ultrasound images. Biomed. Signal Process. Control 2018, 40, 117–130.
  3. Tessler, F.N.; Middleton, W.D.; Grant, E.G.; Hoang, J.K.; Berland, L.L.; Teefey, S.A.; Cronan, J.J.; Beland, M.D.; Desser, T.S.; Frates, M.C.; et al. ACR thyroid imaging, reporting and data system (TI-RADS): White paper of the ACR TI-RADS committee. J. Am. Coll. Radiol. 2017, 14, 587–595.
  4. Ma, J.; Wu, F.; Zhu, J.; Xu, D.; Kong, D. A pretrained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 2017, 73, 221–230.
  5. Pedraza, L.; Vargas, C.; Narvaez, F.; Duran, O.; Munoz, E.; Romero, E. An open access thyroid ultrasoundimage database. In Proceedings of the 10th International Symposium on Medical Information Processing and Analysis, Cartagena de Indias, Colombia, 28 January 2015; pp. 1–6.
  6. Zhu, Y.; Fu, Z.; Fei, J. An image augmentation method using convolutional network for thyroid nodule classification by transfer learning. In Proceedings of the 3rd IEEE International Conference on Computer and Communication, Chengdu, China, 13–16 December 2017; pp. 1819–1823.
  7. Nguyen, D.T.; Pham, D.T.; Batchuluun, G.; Yoon, H.S.; Park, K.R. Artificial intelligence-based thyroid nodule classification using information from spatial and frequency domains. J. Clin. Med. 2019, 8, 1976.
  8. Vuong, Q.H.; Ho, M.T.; Vuong, T.T.; La, V.P.; Ho, M.T.; Nghiem, K.C.P.; Tran, B.X.; Giang, H.H.; Giang, T.V.; Latkin, C.; et al. Artificial intelligence vs. natural stupidity: Evaluating AI readiness for the Vietnamese medical information system. J. Clin. Med. 2019, 8, 168.
  9. Havaei, M.; Davy, A.; Warde-Farley, D.; Biard, A.; Courville, A.; Bengio, Y.; Pal, C.; Jodoin, P.-M.; Larochelle, H. Brain tumor segmentation with deep neural networks. Med. Image Anal. 2017, 35, 18–31.
  10. Bhandary, A. et al. Deep-learning framework to detect lung abnormality – A study with chest x-ray and lung CT scan images. Pattern Recogn. Lett. 2020, 129, 271–278.
  11. Kamnitsas, K.; Ledig, C.; Newcombe, V.F.J.; Simpson, J.P.; Kane, A.D.; Menon, D.K.; Rueckert, D.; Glocker, B. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 2017, 36, 61–78.
  12. Zhu, Y.; Fu, Z.; Fei, J. An image augmentation method using convolutional network for thyroid nodule classification by transfer learning. In Proceedings of the 3rd IEEE International Conference on Computer and Communication, Chengdu, China, 13–16 December 2017; pp. 1819–1823.
  13. Vuong, Q.H.; Ho, M.T.; Vuong, T.T.; La, V.P.; Ho, M.T.; Nghiem, K.C.P.; Tran, B.X.; Giang, H.H.; Giang,T.V.; Latkin, C.; et al. Artificial intelligence vs. natural stupidity: Evaluating AI readiness for the Vietnamese medical information system. J. Clin. Med. 2019, 8, 168.Sensors 2020, 20, 1822 21 of 23
  14. Havaei, M.; Davy, A.; Warde-Farley, D.; Biard, A.; Courville, A.; Bengio, Y.; Pal, C.; Jodoin, P.-M.; Larochelle, H. Brain tumor segmentation with deep neural networks. Med. Image Anal. 2017, 35, 18–31.
  15. Bhandary, A. et al. Deep-learning framework to detect lung abnormality – A study with chest x-ray and lung CT scan images. Pattern Recogn. Lett. 2020, 129, 271–278.
  16. Pedraza, L.; Vargas, C.; Narvaez, F.; Duran, O.; Munoz, E.; Romero, E. An open access thyroid ultrasoundimage database. In Proceedings of the 10th International Symposium on Medical Information Processing and Analysis, Cartagena de Indias, Colombia, 28 January 2015; pp. 1–6.
  17. Introduction to Convolutional Neural Networks (CNN), Oct. 2022, [Online]. Available: https://www.analyticsvidhya.com/blog/2021/05/convolutional-neural-networks-cnn/
  18. Nikhila, Ponugoti, et al. "Lightweight residual network for the classification of thyroid nodules." arXiv preprint https://arxiv.org/abs/1911.08303 (2019).
  19. Guan, Qing, et al. "Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study." Annals of translational medicine 7.7 (2019).
  20. Duong-Trung, Nghia, Dung Ngoc Le Ha, and Hiep Xuan Huynh. "Classification-Segmentation Pipeline for MRI via Transfer Learning and Residual Networks." RICE. 2021.
  21. Kassjański, Michał, Marcin Kulawiak, and Tomasz Przewoźny. "Development of an AI-based audiogram classification method for patient referral." 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS). IEEE, 2022.
  22. Treigys, Povilas, et al. "Detecting Cancerous Regions in DCE MRI using Functional Data, XGboost and Neural Networks." Annals of Computer Science and Information Systems 32 (2022): 23-30.