CapsNet-COVID19: Lung CT image classification method based on CapsNet model

XiaoQing Zhang; GuangYu Wang; Shu-Guang Zhao; XiaoQing Zhang; GuangYu Wang; Shu-Guang Zhao

doi:10.3934/mbe.2022236

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 5: 5055-5074. doi: 10.3934/mbe.2022236

Previous Article Next Article

Research article Special Issues

CapsNet-COVID19: Lung CT image classification method based on CapsNet model

1.
Nanjing University of Science and Technology, Taizhou Technology Institute, Taizhou 225300, China
2.
Donghua University, College of Information Science and Technology, Shanghai 201620, China

Academic Editor: Simon James Fong

Received: 16 December 2021 Revised: 27 February 2022 Accepted: 08 March 2022 Published: 16 March 2022

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.
- COVID-19,
- CT image analysis of lungs,
- medical image segmentation,
- medical image classification,
- capsule network,
- convolutional neural network
Citation: XiaoQing Zhang, GuangYu Wang, Shu-Guang Zhao. CapsNet-COVID19: Lung CT image classification method based on CapsNet model[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 5055-5074. doi: 10.3934/mbe.2022236

Related Papers:

Abstract

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.

References

[1]	T. B. Chandra, K. Verma, B. K. Singh, D. Jain, S. S. Netam, Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble, Expert Syst. Appl., 165 (2021), 113909. https://doi.org/10.1016/j.eswa.2020.113909 doi: 10.1016/j.eswa.2020.113909
[2]	L. Wang, Z. Q. Lin, A. Wong, COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, Sci. Rep., 10 (2020), 19549. https://doi.org/10.1038/s41598-020-76550-z doi: 10.1038/s41598-020-76550-z
[3]	M. Y. Ng, E. Y. P. Lee, J. Yang, F. Yang, X. Li, H. Wang, et al., Imaging profile of the COVID-19 infection: radiologic findings and literature review, Radiol. Cardiothorac. Imaging, 2 (2020), e200034. https://doi.org/10.1148/ryct.2020200034
[4]	C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395 (2020), 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5
[5]	W. Guan, Z. Ni, Y. Hu, W. Liang, C. Ou, J. He, et al., Clinical characteristics of coronavirus disease 2019 in china, N. Engl. J. Med., 382 (2020), 1708-1720. https://doi.org/10.1056/NEJMoa2002032
[6]	A. Tao, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, et al., Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases, Radiology, 296 (2020), E32-E40. https://doi.org/10.1148/radiol.2020200642
[7]	T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, U. R. Acharya, Automated detection of covid-19 cases using deep neural networks with x-ray images, Comput. Biol. Med., 121 (2020), 103792. https://doi.org/10.1016/j.compbiomed.2020.103792 doi: 10.1016/j.compbiomed.2020.103792
[8]	Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, S. Singh, P. K. Shukla, Deep transfer learning based classification model for Covid-19 disease, Ing. Rech. Biomed., 2020. https://doi.org/10.1016/j.irbm.2020.05.003 doi: 10.1016/j.irbm.2020.05.003
[9]	A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks, preprint, arXiv: 2003.10849v3.
[10]	A. Oulefki, S. Agaian, T. Trongtirakul, A. K. Laouar, Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images, Pattern Recognit., 114 (2021), 107747. https://doi.org/10.1016/j.patcog.2020.107747 doi: 10.1016/j.patcog.2020.107747
[11]	C. Zhao, Y. Xu, Z. He, J. Tang, Y. Zhang, J. Han, et al., A new approach for lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images, Pattern Recognit., 119 (2021), 108071. https://doi.org/10.1016/j.patcog.2021.108071
[12]	J. He, Q. Zhu, K. Zhang, P. Yu, J. Tang, An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation, Appl. Soft Comput., 113 (2021), 107947. https://doi.org/10.1016/j.asoc.2021.107947 doi: 10.1016/j.asoc.2021.107947
[13]	N. Mu, H. Wang, Y. Zhang, H. Yang, J. Tang, Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images, Pattern Recognit., 120 (2021), 108168. https://doi.org/10.1016/j.patcog.2021.108168 doi: 10.1016/j.patcog.2021.108168
[14]	Q. Mao, S. Zhao, L. Ren, Z. Li, D. Tong, X. Yuan, et al., Intelligent immune clonal optimization algorithm for pulmonary nodule classification, Math. Biosci. Eng., 18 (2021), 4146-4161. https://doi.org/10.3934/mbe.2021208
[15]	X. Liu, Q. Yuan, Y. Gao, S. Wang, X. Tang, J. Tang, et al., Weakly supervised segmentation of COVID-19 infection with scribble annotation on CT images, Pattern Recognit., 122 (2022), 108341. https://doi.org/10.1016/j.patcog.2021.108341
[16]	K. Zhang, X. H. Liu, J. Shen, Z. Li, Y. Sang, X. Wu, et al., Clinically applicable AI system for accurate diagnosis, quantitative measurements and prognosis of COVID-19 pneumonia using computed tomography, Cell, 181 (2020). https://doi.org/10.1016/j.cell.2020.04.045
[17]	H. X. Guan, Y. Xiong, N. Q. Shen, The novel coronavirus pneumonia (COVID-19) clinical imaging features, Radiol. Pract., 35 (2020),
[18]	O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation, in Medical Image Computing and Computer-Assisted Intervention, 9351 (2015), 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
[19]	S. Sabour, N. Frosst, G. E. Hinton, Dynamic routing between capsules, preprint, arXiv: 1710.09829
[20]	N. Abraham, N. M. Khan, A novel focal tversky loss function with improved attention u-net for lesion segmentation, in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, (2019), 683-687. https://doi.org/10.1109/ISBI.2019.8759329
[21]	Y. Pan, H. Wang, H. Wang, Pneumonia lesion segmentation technology based on deep learning for quantitative analysis of novel coronavirus pneumonia, Chin. J. Med. Comput. Imaging, 26 (2020).
[22]	A. Amyar, R. Modzelewski, H. Li, S. Ruan, Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation, Comput. Biol. Med., 126 (2020), 104037. https://doi.org/10.1016/j.compbiomed.2020.104037 doi: 10.1016/j.compbiomed.2020.104037
[23]	J. Zhao, Y. Zhang, X. He, P. Xie, Covid-ct-dataset: A CT scan dataset about Covid-19, preprint, arXiv: 2003.13865.
[24]	S. Wang, B. Kang, J. L. Ma, X. Zeng, M. Xiao, J. Guo, et al., A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19), Eur. Radiol., 31 (2020), 6096-6104. https://doi.org/10.1007/s00330-021-07715-1

Reader Comments

Your name:*

Email:*
© 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)