1. ACPS
  2. All Volumes and Issues
  3. Volume 8, Number 2, 2023
  4. Covid-19 Diagnosis Using Deep Learning From X-Ray and CT Images – Overview

Covid-19 Diagnosis Using Deep Learning From X-Ray and CT Images – Overview

ACPS.
2023;
Volume 8, Number 2
: pp. 126 - 132
https://doi.org/10.23939/acps2023.02.126
Authors:
  1. Magdalena Michalska-Ciekańska
  2. Oksana Boyko
1
Institute of Electronics and Information Technology, Lublin University of Technology
2
Department of Medical Informatics, Danylo Halytsky Lviv National Medical University

Since the outbreak of the pandemic in 2019, Covid-19 has become one of the most important topics in the field of medicine. This disease, caused by the SARS- CoV-2 virus, can lead to serious respiratory diseases and other complications. They can even lead to death. In recent years, the number of Covid-19 cases around the world has increased significantly, resulting in the need for rapid and effective diagnosis of the disease. Currently, the use of deep learning in medical diagnostics is becoming more and more common. It provides the high diagnostic efficacy that scien- tists, doctors and patients care about. During the Covid-19 diagnostic procedure, most clinicians order images from X- ray and CT to be taken from patients. It is the analysis of these images that gives a full diagnosis. In this article, we will discuss the use of deep neural networks in the diagnosis of Covid-19, especially using chest images taken from X-ray and CT.

deep learning
convolutional neural net- works
Covid-19 diagnostics
CT scans
chest X-ray
  1. N. Chen, M. Zhou, X. Dong et al., “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavi- rus pneumonia in Wuhan, China: a descriptive study,” The Lancet, 395 (10223), 2020, pp. 507–513. DOI: 10.1016/S0140-6736(20)30211-7
  2. C. M. A. De O. Lima, “Information about the new coro- navirus disease (COVID-19),” Radiologia  Brasileira  53 (2), 2020. DOI: 10.1590/0100-3984.2020.53.2e1
  3. T. Struyf, J. J. Deeks, J. Dinnes et al., “Signs and symptoms to determine if a patient presenting in primary care or hospi- tal outpatient settings has COVID-19 disease,” Cochrane Database of Systematic Reviews 7 (7), 2020.
  4. Organization, W. H., WHO Coronavirus Disease (COVID- 19) Dashboard, World Health Organization, Geneva, Swit- zerland, 2020, http://Https://Covid19.%20Who.%20Int/
  5. L. Wang, Z. Lin, and A. Wang, “COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Scientific Re- ports 10, Article ID 19549, 2020.
  6. T. Ai, Z. Yang, H. Hou 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 (2), Arti- cle ID 200642, 2020. DOI: 10.1148/radiol.2020200642
  7. R. Tadeusiewicz, M. Szaleniec: “Leksykon sieci neu- ronowych”, Wydawnictwo Fundacji Projekt Nauka, Wro- cław, 2015.
  8. Sieć neuronowa. https://pl.wikipedia.org/wiki/Sie%C4%87_ neuronowa [accessed on 30.03.2022]
  9. W. Zhang, “Shift–invariant pattern recognition neural net- work and its optical architecture”, Proceedings of Annual Conference of the Japan Society of Applied Physics, 1998.
  10. F. Chollet, “Deep Learning. Praca z językiem Python i biblioteką Keras”, Helion, 2019
  11. M. Mamczur, “Jak działają konwolucyjne sieci neuronowe”, https://miroslawmamczur.pl/jak–dzialaja–konwolucyjne– sieci–neuronowe–cnn/[ accessed on 17.05.2022]
  12. . Dzierżak, “Zastosowanie deep learningu w analizie obrazów medycznych” in Prace doktorantów Wydziału Elektrotechniki i Informatyki Politechniki Lubelskiej, 2018, 59–70
  13. X. Lu, Y. Firoozeh Abolhasani Zadeh, “Deep learning- based classification for melanoma detection using Xcep- tionNet”, Journal of Healthcare Engineering 2022, 2196096. DOI:10.1155/2022/2196096
  14. X. Lu, Y. Firoozeh Abolhasani Zadeh, “Deep learning- based classification for melanoma detection using Xcep- tionNet”, Journal of Healthcare Engineering 2022, 2196096. DOI: 10.1155/2022/2196096
  15. S. Chaturvedi, J. Tembhurne, T, Diwan, “A multi-class skin cancer classification using deep convolutional neural networks”. Multimedia Tools and Applications 2020, 79. DOI:10.1007/s11042-020-09388-2
  16. S. Dinggang, W. Guorong., S. Heung-Il, “Deep Learning in Medical Image Analysis”, The Annual Review of Bio- medical Engineering, 2017, 9, pp. 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
  17. G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian., J. van der Laak, B. Ginneken B., C. Sánchez, “A survey on deep learning in medical image analysis”, Medical Image Analysis 2017, 42, pp. 60–88. DOI: 10.1016/j.media.2017.07.005
  18. M. Avendi, A. Kheradvar, H. Jafarkhani, “A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI”,  Medical  Image  Analysis  2016,  30,  pp.108–119. DOI: 10.1016/j.media.2016.01.005
  19. A. Krizhevsky, I. Sutskever, G. Hinton, “Imagenet classi- fication with deep con- volutional neural networks”, Pro- ceedings of the Advances in Neural Information Process- ing Systems, 2012, pp. 1097–1105. DOI:10.1145/3065386
  20. R. Siegel, D. Naishadham, A. Jemal, “Cancer statistics”, CA Cancer J Clin., 2013, 63(1), pp. 11–30. DOI: 10.3322/caac.21166
  21. T. Alafif, A. Tehame, S. Bajaba, A. Barnawi, S. Zia, “Ma- chine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Direc- tions”, Int. J. Environ. Res. Public Health 2021, 18, 1117. DOI: 10.3390/ijerph18031117
  22. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, 521 (7553),2015, pp. 436–444. https://doi.org/ 10.1038/nature14539.
  23. M. Scudellari, Hospitals Deploy AI Tools to Detect COVID- 19 on Chest Scans, 2020. Available online: https://spectrum.ieee.org/the-human-os/biomedical/imaging/ hospitals-deploy-ai-tools-detect-covid19-chest-scans (ac- cessed on 10 September 2020).
  24. S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, et al. “A deep learning algo- rithm using CT images to screen for Corona Virus Disease (COVID-19)”, medRxiv 2020. DOI: 10.1007/s00330-021- 07715-1
  25. J. Cohen, L. Dao, P. Morrison, K. Roth, Y. Bengio, B. Shen, A. Abbasi, M. Hoshmand-Kochi, M. Ghassemi, H. Li, et al. “Predicting covid-19 pneumonia severity on chest X-ray with deep learning”, arXiv 2020, arXiv:2005.11856. https://doi.org/10.48550/arXiv.2005.11856
  26. BBVA. Artificial Intelligence to detect COVID-19 in Less than  a  Second  Using  X-rays.  2019.  Available  online: https://www.bbva.com/en/artificial-intelligence-to-detect- covid-19-in-less-than-a-second-using-x-rays/ (accessed on 6 September 2020).
  27. M. Pandit, S. Banday, “SARS n-CoV2-19 detection from chest X-ray images using deep neural networks”, Int. J. Pervasive   Comput.   Commun.   2020,   16,   pp.419–427. DOI:10.1108/ijpcc-06-2020-0060
  28. S, Basu, S. Mitra, “Deep Learning for Screening COVID-19 using Chest X-ray Images”, arXiv 2020, arXiv:2004.10507 https://doi.org/10.48550/arXiv.2004.10507
  29. L. Wang and A. Wong, “COVID-Net: A tailored  deep convolutional neural network design for detection of COVID-19 cases from chest radiography images,” arXiv preprint arXiv:2003.09871, 2020. DOI: 10.1038/s41598- 020-76550-z
  30. S. Basu and S. Mitra, “Deep Learning for Screening COVID-19 using Chest X-Ray Images,” arXiv preprint arXiv:2004.10507, 2020. DOI: 10.1038/s41598-020-