Skip to main content
SpringerLink
Log in

Computational Intelligence Methods in Medical Image-Based Diagnosis of COVID-19 Infections

  • Chapter
  • First Online:
Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 923))

Abstract

The coronavirus disease (COVID-19) has been identified and widely known as an invisible enemy in the history of mankind. The novel coronavirus continued to pose a significant risk to health, and even death of human on earth. Originated December 2019, in the Wuhan province of China, now vastly spreading around the globe like wildfire. The coronavirus pandemic caused the largest global largest recession in the history of mankind, with the largest global population at the time being placed on lockdown, quarantine, and isolation due to vastly spread of the pandemic. The initial stages of this outbreak, all over the countries around the globe, including the USA, faced one major threat—a lack of diagnostic tools and proper testing. In this context, the first step in medical practice is Diagnosis, which is very crucial for clinical decision making. Researchers have used different computational intelligence techniques to classify different types of diseases, such as Diabetes, Cancer, Epilepsy, Lungs, heart disease and Liver, etc., therefore, COVID-19 should not be an exception. This Chapter will systematically talk about the recent state-of-the-art computational intelligence (CI) approaches in the field of medical diagnosis of COVID-19 based on the medical image.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. McFall, J., Uliana, K., & Lauren Frias, M. (2020, March 14). A third of the global population is on coronavirus lockdown—Here’s our constantly updated list of countries and restrictions, Business Insider Australia.

    Google Scholar 

  2. Greenspan, H., Van, G. B., & Summers, R. M. (2016). Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5), 1153–1159.

    Article  Google Scholar 

  3. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei, F. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition.

    Google Scholar 

  4. Weinstein, E. A., et al. (2013). The cancer genome atlas pan-cancer analysis project. Nature Genetics, 45(10), 11–13.

    Article  Google Scholar 

  5. Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395.

    Google Scholar 

  6. National Academies of Sciences and Medicine (2016). Improving diagnosis in health care, National Academies Press.

    Google Scholar 

  7. Cios, K. J., & Moore, G. W. (2002). Uniqueness of medical data mining. Artificial Intelligence in Medicine, 26(1), 1–24.

    Article  Google Scholar 

  8. Jain, A. K., Mao, J., & Mohiuddin, K. (1996). Artificial neural networks: A tutorial. Computer, 3, 31–44.

    Google Scholar 

  9. World Health Organization. (2018). “Infectious Disease,” WHO.

    Google Scholar 

  10. Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine—Beyond the peak of. New England Journal of Medicine, 376(26), 2507–2509.

    Article  Google Scholar 

  11. Boon, I. S., Yong, T. T., & Boon, C. S. (2018). Assessing the role of artificial intelligence (AI) in clinical oncology: Utility of machine learning in radiotherapy target volume delineation. Medicines (Basel), 5(4).

    Google Scholar 

  12. Im, H., Pathania, D., McFarland, P. J., Sohani, A. R., Degani, I., Allen, M., et al. (2018). Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning. Nature Biomedical Engineering, 2(9), 666–674.

    Article  Google Scholar 

  13. Martini, M., Gazzaniga, V., Bragazzi, N. L., & Barberis, I. (2019). The Spanish influenza pandemic: A lesson from history 100 years after 1918. Journal of Preventive Medicine and Hygiene, 60, E64–E67.

    Google Scholar 

  14. Duncan, C. J., & Scott, S. (2005). What caused the black death? [CrossRef] [PubMed]. Postgraduate Medical Journal, 81, 315.

    Article  Google Scholar 

  15. CDC. Influenza (flu). Retrieved on April 25, 2020, from https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html.

  16. Song, Z., Xu, Y., Bao, L., Zhang, L., Yu, P., Qu, Y., Zhu, H., Zhao, W., Han, Y., & Qin, C. (2019). From sars to mers, thrusting coronaviruses into the spotlight. Viruses, 11, 59.

    Google Scholar 

  17. Sun, G., Matsui, T., Hakozaki, Y., & Abe, S. (2015). An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method. Journal of Infection, 70(3), 230–236.

    Article  Google Scholar 

  18. Fraley, S. I., Athamanolap, P., Masek, B. J., Hardick, J., Carroll, K. C., Hsieh, Y. H., et al. (2016). Nested machine learning facilitates increased sequence content for large-scale automated high resolution melt genotyping. Scientific Reports, 6.

    Google Scholar 

  19. Saybani, M. R., Shamshirband, S., Hormozi, S. G., Wah, T. Y., Aghabozorgi, S., Pourhoseingholi, M. A., & Olariu, T. (2015). Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system. Iranian Red Crescent Medical Journal, 17(4), e24557.

    Article  Google Scholar 

  20. Watkins, A., & Boggess, L. C. (2002). A new classifier based on resource limited artificial immune systems. In Proceedings of Congress on Evolutionary Computation IEEE World Congress on Computational Intelligence, Honolulu.

    Google Scholar 

  21. Cuevas, E., Osuna-Enciso, V., Zaldivar, D., Perez-Cisneros, M., & Sossa, H. (2012). Multi-threshold segmentation based on artificial immune systems. Mathematical Problems in Engineering, 20.

    Google Scholar 

  22. Go, T., Kim, J. H., Byeon, H., & Lee, S. J. (2018). Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells. Journal of Biophotonics, 11(9), e201800101.

    Article  Google Scholar 

  23. Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K., et al. (2020). Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance, 25.

    Google Scholar 

  24. Gozes, O., Frid-Adar, M., Greenspan, H., Brownin, D. P., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. (2020). Rapid AI development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv:2003.05037.

  25. Rajpurkar, P., Irvin J., Zhu, K., Yang, B., Mehta, H., Duan, et al. (2017). Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225.

  26. Charmaine, B., Jagpal, G., David, C., & Benson, A. B. (2020). Deep learning system to screen coronavirus disease 2019 pneumonia. Applied Intelligence.

    Google Scholar 

  27. Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020). Deep learning-based detection for covid-19 from chest ct using weak label, medRxiv.

    Google Scholar 

  28. Chowdhury, M. E., Rahman, T., Khandakar, A., Mazha, R., Kadir, M. A., Mahbub, Z. B., et al. (2020). Can AI help in screening viral and covid-19 pneumonia? arXiv:2003.13145.

  29. Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., et al. (2020). Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology, 200905.

    Google Scholar 

  30. Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., et al. (2020). Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images, medRxiv.

    Google Scholar 

  31. Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., et al. (2020). A deep learning algorithm using ct images to screen for corona virus disease (covid-19), medRxiv.

    Google Scholar 

  32. Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., et al. (2020). Infection quantification of covid-19 in ct images with deep learning. arXiv:2003.04655.

  33. Chen, I., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., et al. (2020). Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study, medRxiv.

    Google Scholar 

  34. Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., et al. (2020). Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv:2003.09860.

  35. Bernheim, A., Mei, X., Huang, M., Yang, Y., Faya, Z. A., Zhang, N., et al. (2020). Chest ct findings in coronavirus disease-19 (covid-19): relationship to duration of infection. Radiology, 200463.

    Google Scholar 

  36. Gozes, O., Frid-Adar, M., Greenspan, H., Browni, P. D., Zhang, H., Ji, W., et al. (2020). Rapid AI development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv:2003.05037.

  37. Bertolini, D., Teixeira, L. O., Silla, C. N. Jr., & Costa, Y. G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. arXiv:2004.05835.

  38. Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine.

    Google Scholar 

  39. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., et al. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology, 200642.

    Google Scholar 

  40. Cohen, J. P., Hashir, M., Brooks, R., & Bertrand, H. (2020). On the limits of cross-domain generalization in automated X-ray prediction. arXiv:2002.02497.

  41. Makoul, G., Curry, R. H., & Tang, P. C. (2001). The use of electronic medical records: Communication patterns in outpatient encounters. [CrossRef] [PubMed]. Journal of the American Medical Informatics Association, 8, 610–615.

    Article  Google Scholar 

  42. Westin, A., Krane, D., Capps, K., & Peterson, T. (2012). Making it meaningful: How consumers value and trust health it survey; national partnership for women & families, Washington, DC, USA.

    Google Scholar 

  43. Xuehai, H., Xingyi, Y., Shanghang, Z., Jinyu, Z., Yichen, Z., & Eric, X. (2020). Sample-efficient deep learning for COVID-19, medRxiv.

    Google Scholar 

  44. Lannquist, Y. (2018). Ethical & policy risks of artificial intelligence in healthcare. Retrieved on May 18, 2020, from https://www.thefuturesociety.org/ethical-policy-risks-of-artificialintelligence-in-healthcare/.

  45. Lufkin, B. (2017). Why the biggest challenge facing AI is a ethical one. Retrieved May 22, 2020, from https://www.bbc.com/future/story/20170307-the-ethical-challenge-facingartificial-intelligence?obOrigUrl=true.

  46. Raza, K., & Singh, N. K. (2018). A tour of unsupervised deep learning for medical image analysis. arXiv:1812.07715.

  47. Disruptive dozen. Cambridge (MA): World Medical Innovation Forum (2018). Retrieved on May 25, 2020, from https://worldmedicalinnovation.org/wp-content/uploads/2018/04/Partners-FORUM-2018-BROCHURE-D12-AI-180410_1202-FREV2-FORWEB-X3.pdf.

  48. Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological Physics and Technology, 10(3), 257–273.

    Article  Google Scholar 

  49. Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563–577.

    Article  Google Scholar 

  50. Hatt, M., Tixier, F., Visvikis, D., & Cheze Le Rest, C. (2017). Radiomics in PET/CT: More than meets the eye? Journal of Nuclear Medicine, 58(3), 365–366.

    Article  Google Scholar 

  51. Krittanawong, C., Tunhasiriwet, A., Zhang, H., Wang, Z. A., Aydar, M., & Kitai, T. (2017). Deeplearning with unsupervised feature in echocardiographic imaging. Journal of American College of Cardiology, 69(16), 2100–2101.

    Google Scholar 

  52. Brady, A. P. (2017). Error and discrepancy in radiology: Inevitable or avoidable? Insights Imaging, 8, 171–182.

    Article  Google Scholar 

  53. Paredes, M. (2018). Can artificial intelligence help reduce human medical error? Two examples from ICUs in the US and Peru.

    Google Scholar 

  54. Loria, K. (2018) Putting the AI in radiology. Radiology Today, 19(1).

    Google Scholar 

  55. Davenport, T. H., & Dreyer, K. J. (2018). AI will change radiology, but it won’t replace radiologists. Harvard Business Review.

    Google Scholar 

  56. Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., & Barfett, J. (2018). Canadian association of radiologists white paper on artificial intelligence in radiology. Canadian Association of Radiologists Journal, 69(2), 120–135.

    Article  Google Scholar 

  57. Medical imaging in the age of artificial intelligence. Erlangen (DE), Siemens (2017). Retrieved on May 19, 2020, from https://www.siemens.com/press/pool/de/events/2017/healthineers/2017-11-rsna/white-paper-medical-imaging-in-the-age-ofartificial-intelligence.pdf.

  58. FDA permits marketing of clinical decision support software for alerting providers of a potential stroke in patients, U.S. Food & Drug Administration (2018). Retrieved on May 24, 2018, from https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm596575.htm.

  59. Harvey, H. (2017). Nightmare on ML street: the dark potential of AI in radiology. Retrieved on May 15, 2020, from https://towardsdatascience.com/nightmare-on-ml-street-the-darkpotential-of-ai-in-radiology-71074e70da91.

  60. Paiva, O. A., & Prevedello, L. M. (2017). The potential impact of artificial intelligence in radiology. Radiologia brasileira, 50(5).

    Google Scholar 

  61. Harrington, S. G., & Johnson, M. K. (2018). The FDA and artificial intelligence in radiology: Defining new boundaries. Journal of American College of Radiology, 18, 31343–31347.

    Google Scholar 

Download references

Acknowledgements

Thanks to all experts who have contributed in one way or the other towards completion of this work. The content is solely the responsibility of the author and does not necessarily reflect the views of anybody.

Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, Faculty of Science, University of Maiduguri, Maiduguri, Nigeria

    Abdullahi Isa

Authors
  1. Abdullahi Isa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdullahi Isa .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi, India

    Khalid Raza

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Isa, A. (2021). Computational Intelligence Methods in Medical Image-Based Diagnosis of COVID-19 Infections. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-15-8534-0_13

Download citation

Publish with us

Policies and ethics

Search

Navigation