Volume 14 - Journal of Control, Vol. 14, No. 5, Special Issue on COVID-19
JoC 2021, 14 - Journal of Control, Vol. 14, No. 5, Special Issue on COVID-19: 1-14 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Karsaz A. Evaluation of Lung Involvement in Patients with Coronavirus Disease from Chest CT Images Using Multi-Objective Self-Adaptive Differential Evolution Approach. JoC 2021; 14 (S1) :1-14
URL: http://joc.kntu.ac.ir/article-1-832-en.html
URL: http://joc.kntu.ac.ir/article-1-832-en.html
Khorasan Institute of Higher Education
Abstract: (12268 Views)
Under the global pandemic of COVID-19 over the last year, the use of image processing techniques and the artificial intelligence algorithms to analysis chest X-ray (CXR) images is becoming important. Determining the lung involvement and percentage development of COVID-19 is one of most important requirements for the hospitalization centers. The most studies in this field belong to the articles based on the deep learning methodologies using convolution neural networks, which are usually implemented to facilitate the screening process. Only a few number of studies are about the determining the percentage of lung involvement and development of coronavirus based on CXR images. The lack of comprehensive datasets of CT images with a large amount of samples is one of the most important issues in this field. Determining of lung infection in COVID-19 patients, based on different CXR images in different days, has its own challenges such as different image sizes, illumination density, radiation dose of X-ray and angle of radiation, which makes it impossible to the implement a simple differential filter on two different images. Using an optimization self-adaptive algorithm with differential and multi-objective approach can improve the performance accuracy with a corresponding reduction in computation time.
Keywords: COVID-19, lung involvement, chest X-ray images, image processing, multi-objective self-adaptive differential evolution algorithm.
Type of Article: Research paper |
Subject:
COVID-19
Received: 2021/01/19 | Accepted: 2021/02/13 | Published: 2021/02/28
Received: 2021/01/19 | Accepted: 2021/02/13 | Published: 2021/02/28
References
1. N. Chen, M. Zhou, X. Dong, J. Qu, F. Gong, Y. Han, Y. Qiu, J. Wang, Y. Liu, Y. Wei et al., "Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: a descriptive study," The Lancet, vol. 395, no. 10223, pp. 507-513, 2020. [DOI:10.1016/S0140-6736(20)30211-7]
2. J. D. Arias-Londoño, J. A. Gómez-García, L. Moro-Velázquez and J. I. Godino-Lorente, "Artificial intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach," in IEEE Access, pp. 1-1, 2020, doi: 10.1109/ACCESS.2020.3044858. [DOI:10.1109/ACCESS.2020.3044858]
3. H. A. Rothan and S. N. Byrareddy, "The epidemiology and pathogenesis of coronavirus disease (covid-19) outbreak," Journal of autoimmunity, p.102433, 2020. [DOI:10.1016/j.jaut.2020.102433]
4. Y. Pan, H. Guan, S. Zhou, Y. Wang, Q. Li, T. Zhu, Q. Hu, and L. Xia, "Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-ncov): a study of 63 patients in wuhan, china," European radiology, pp. 1-4, 2020. [DOI:10.1007/s00330-020-06731-x]
5. C. CDC Weekly, "The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) - China, 2020", China CDC Weekly, vol. 2, no. 8, pp. 113-122, 2020. Available: 10.46234/ccdcw2020.032. [DOI:10.46234/ccdcw2020.032]
6. P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya et al., "Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning," arXiv preprint arXiv:1711.05225, 2017.
7. A. Narin, C. Kaya, and Z. Pamuk, "Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks," arXiv preprint arXiv:2003.10849, 2020.
8. E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, "Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images," arXiv preprint arXiv:2003.11055, 2020.
9. L. ang, Z. Q. Lin, and A. Wong, "Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images," Scientific Reports, vol. 10, no. 19549, 2020. [DOI:10.1038/s41598-020-76550-z]
10. M. Z. Islam, M. M. Islam, and A. Asraf, "A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images," Informatics in Medicine Unlocked, vol. 20, p. 100412, 2020. [DOI:10.1016/j.imu.2020.100412]
11. J. Civit-Masot, F. Luna-Perejón, M. D. Morales, and A. Civit, "Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images," Applied Sciences, vol. 10, no. 13, 2020. [DOI:10.3390/app10134640]
12. A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, "CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection," IEEE Access, vol. 8, pp. 91 916- 91 923, 2020. [DOI:10.1109/ACCESS.2020.2994762]
13. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, "Pytorch: An imperative style, highperformance deep learning library," in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, pp. 8024-8035. [Online]. Available: http://papers.neurips.cc/paper/ 9015-pytorch-an-imperative-style-high-performance-deep-learning-library. Pdf
14. H. Y. F. Wong et al., "Frequency and distribution of chest radiographic findings in COVID-19 positive patients," Radiology, Mar. 2020, Art. no. 201160.
15. Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., Zheng, D., Wang, J., Hesketh, R., Yang, L. and Zheng, C., 2020. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology, 295(3), pp.715-721. [DOI:10.1148/radiol.2020200370]
16. Bankier, A., MacMahon, H., Goo, J., Rubin, G., Schaefer-Prokop, C. and Naidich, D., 2017. Recommendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society. Radiology, 285(2), pp.584-600. [DOI:10.1148/radiol.2017162894]
17. S.-I. S. O. M. A. I. Radiology. (2020). COVID-19 Database. [Online]. Available: https://www.sirm.org/category/senza-categoria/covid-19/
18. J. C. Monteral. (2020). COVID-Chestxray Database. [Online]. Available: https://github.com/ieee8023/covid-chestxray-dataset.
19. P. Mooney. (2018). Chest X-Ray Images (Pneumonia). [Online]. Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.
20. S. Mirjalili, "Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems," Neural Computing and Applications, vol. 27, pp. 1053-1073, 2016. [DOI:10.1007/s00521-015-1920-1]
21. Storn, R. and Price, K. 1995, Differential Evolution- A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report, International Computer Science Institute, Berkeley, CA.
22. Q. Fan and X. Yan, "Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies," in IEEE Transactions on Cybernetics, vol. 46, no. 1, pp. 219-232, Jan. 2016 [DOI:10.1109/TCYB.2015.2399478]
23. J. Brest, V. Zumer and M. S. Maucec, "Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization," 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, 2006, pp. 215-222, [DOI:10.1109/CEC.2006.1688311]
24. J. Wang, G. Liang and J. Zhang, "Cooperative Differential Evolution Framework for Constrained Multiobjective Optimization," in IEEE Transactions on Cybernetics, vol. 49, no. 6, pp. 2060-2072, June 2019, [DOI:10.1109/TCYB.2018.2819208]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
