research-article

Automatic Thyroid Ultrasound Image Detection and Classification with Priori Knowledge

Authors:

Zicheng HuangAuthors Info & Claims

CSAE '21: Proceedings of the 5th International Conference on Computer Science and Application Engineering

Article No.: 91, Pages 1 - 6

https://doi.org/10.1145/3487075.3487166

Published: 07 December 2021 Publication History

Abstract

Medical ultrasonic imaging technology is currently the preferred method to detect and diagnose benign and malignant thyroid nodules, which is widely used because of their low cost and non-invasive damage to patients. But automatic lesion detection and classification on thyroid ultrasound image is quite challenging due to the poor image quality. To solve the problem, based on popular Faster R-CNN network for natural image detection, a ResAt-Faster R-CNN model was proposed in the paper according to the characteristics of thyroid ultrasound image, the residual module and attention mechanism. The medical prior knowledges such as location and distribution information are further introduced to constrain the model to reduce the interference of surrounding tissues. The experimental results demonstrated that our proposed method was effective in the discrimination of thyroid nodules.

References

[1]

B. Freddie, F. Jacques, S. Isabelle, (2018). Global Cancer Statistics: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, vol. 68, pp. 394-424.

[2]

Tingting Zhang, Ning Qu, Pu Zheng, el al. (2017). Application of Machine Learning in Diagnosis and Treatment of Thyroid tumors. Chinese Journal of Cancer, 27(12), 992-995.

[3]

Xuan Gao, Xiaolin Wang (2019). Preliminary Research Progress of Deep Learning in Imaging Medicine. Medical Journal of Fudan University, 46(3), 408-414.

[4]

Bhattacharya, S., Maddikunta, P. K. R., Pham, Q. V., Gadekallu, T. R., Chowdhary, C. L., Alazab, M., & Piran, M. J. (2020). Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustainable cities and society, 102589.

[5]

Lucheng Zhu, Yunliang Ye, Wenhua Luo, (2013). A Model to Discriminate Malignant from Benign Thyroid Nodules Using Artificial Neural Network. PLOS ONE, 8(12): 1-6.

[6]

Ma, J., Wu, F., Zhu, J., (2016). A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics, 73, 221-230.

[7]

Jianning Chi, Ekta Walia, Paul Babyn, (2017). Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network. Journal of Digital Imaging, 30(3), 477-486.

[8]

Ma, J., Wu, F., Zhao, Q., (2017). Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery, 12(11), 1895-1910.

[9]

Tianjiao Liu, Shuaining Xie, Jing Yu, (2017). Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features. IEEE International Conference, 919-923.

[10]

Hailiang Li, Jian Weng, Yujian Shi, (2018). An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Scientific Reports, 8(1), 6600-6612.

[11]

Wenfeng Song, Shuai Li, Ji Liu, (2018). Multi-task Cascade Convolution Neural Networks for Automatic Thyroid Nodule Detection and Recognition. IEEE Journal of Biomedical and Health Informatics, 23(3), 1215-1224.

[12]

Tianjiao Liu, Qianqian Geuo, Chunfeng Lian, el al. (2019). Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Medical Image Analysis.

[13]

Fang, H., (2021). Reliable Thyroid Carcinoma Detection with Real-Time Intelligent Analysis of Ultrasound Images. Ultrasound in Medicine & Biology, 47(3), 590-602.

[14]

He K, Zhang X, Ren S, (2016). Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition.

[15]

Hu J, Shen L, Albanie S, (2017). Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Digital Library

[16]

Ren, S., He, K., Girshick, R., (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis $\&$ Machine Intelligence, 39(6), 1137-1149.

Digital Library

[17]

Goutte C., Gaussier E. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In: Losada D.E., Fernández-Luna J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_25

Digital Library

[18]

Tajbakhsh N, Shin J Y, Gurudu S R, (2016). Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? IEEE Transactions on Medical Imaging, 35(5), 1299-1312.

Cited By

Jiang WChen KLiang ZLuo TYue GZhao ZSong WZhao LWen J(2024)HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAMIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333194428:2(941-951)Online publication date: Feb-2024
https://doi.org/10.1109/JBHI.2023.3331944

Index Terms

Automatic Thyroid Ultrasound Image Detection and Classification with Priori Knowledge
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

CSAE '21: Proceedings of the 5th International Conference on Computer Science and Application Engineering

October 2021

660 pages

ISBN:9781450389853

DOI:10.1145/3487075

Copyright © 2021 ACM.

© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

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Published: 07 December 2021

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CSAE 2021

CSAE 2021: The 5th International Conference on Computer Science and Application Engineering

October 19 - 21, 2021

Sanya, China

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Cited By

Jiang WChen KLiang ZLuo TYue GZhao ZSong WZhao LWen J(2024)HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAMIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333194428:2(941-951)Online publication date: Feb-2024
https://doi.org/10.1109/JBHI.2023.3331944

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