Skip to main content
SpringerLink
Log in

COVID-19 Detection Using Discrete Particle Swarm Optimization Clustering with Image Processing

  • Chapter
  • First Online:
Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis

Abstract

COVID-19 is one of the most transmissible viruses that spread across the world and received global attention. Symptoms of COVID-19 is similar to the chronic disease pneumonia and the lungs become inflammable. Severity of lung involvement with corona virus infection ranges from lack of symptoms to critical disease associated with respiratory failure or death. Image segmentation is the major image processing task and it extracts critical features. The proposed work aims to extract and assess the corona virus disease (COVID-19) affected pneumonia infection in lungs using X-ray images. This research work shows that the input of the computed tomography scan image to detect pneumonia for the early detection of COVID-19, with the blended discrete particle swarm optimization clustering to accurately classify patients as likely die or live. The proposed work accomplished better performance values such as accuracy of 93.7%, sensitivity of 91.3%, and specificity of 97.45%.

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

Access this chapter

Log in via an institution
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

References

  1. Song, F., Shi, N., Shan, F., Zhang, Z., Shen, J., Lu, H., et al. (2020). Emerging coronavirus 2019-nCoV pneumonia. Radiology, 295, 210–217.

    Article  Google Scholar 

  2. Singhal, T. (2020). A review of coronavirus Disease-2019 (COVID-19). Indian Journal of Pediatrics, 87(4), 281–286.

    Article  Google Scholar 

  3. WHO Coronavirus Disease (COVID-19) Dashboard. Available online: https://covid19.who.int/. Online accessed on 5 December 2020.

  4. Zu, Z. Y., Jiang, M. D., Xu, P. P., Chen, W., Ni, Q. Q., Lu, G. M., & Zhang, L. J. (2020). Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology, 296(2), 15–25.

    Google Scholar 

  5. Lee, E. Y., Ng, M. Y., & Khong, P. L. (2020). COVID-19 pneumonia: What has CT taught us? The Lancet Infectious Diseases, 20(4), 384–385.

    Article  Google Scholar 

  6. Bernheim, A., & Mei, X. (2020). Chest CT findings in coronavirus disease-19 (COVID19): Relationship to duration of infection. Radiology, 1(1) 1–19.

    Google Scholar 

  7. Pan, F., & Ye, T. (2020). Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology 3(295), 715--721.

    Google Scholar 

  8. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization, ieee international of first conference on neural networks.

    Google Scholar 

  9. Anderberg, M. R. (1973). The broad view of cluster analysis. Cluster Analysis for Applications, 1(1), 1–9.

    Google Scholar 

  10. Ray, S., & Turi, R. H. (1999). Determination of number of clusters in kmeans clustering and application in colour image segmentation. In Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, pp. 137–143. Calcutta, India.

    Google Scholar 

  11. Li, X., & Fang, Z. (1989). Parallel clustering algorithms. Parallel Computing, 11(3), 275–290.

    Article  MathSciNet  Google Scholar 

  12. Lee, R. C. T. (1981). Clustering analysis and its applications. In Advances in information systems science (pp. 169–292). Springer.

    Chapter  Google Scholar 

  13. Kennedy, J (2000) Stereotyping: Improving particle swarm performance with cluster analysis. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512) (Vol. 2, pp. 1507–1512). IEEE.

    Google Scholar 

  14. Omran, M. G., Engelbrecht, A. P., & Salman, A. (2004). Image classification using particle swarm optimization. In Recent advances in simulated evolution and learning (pp. 347–365). World Scientific.

    Chapter  Google Scholar 

  15. Wang, Y., et al. (2020). Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: A longitudinal study. Thoracic Imaging, 2(296), 55–64.

    Google Scholar 

  16. Shi, H., et al. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: A descriptive study. The Lancet Infectious Diseases, 20(4), 425–434.

    Article  Google Scholar 

  17. Fang, Y., et al. (2020). Sensitivity of chest CT for COVID-19: Comparison to RT-PCR. Radiology.

    Google Scholar 

  18. Bai, H. X., et al. (2020). Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology.

    Google Scholar 

  19. Chua, F., et al. (2020). The role of CT in case ascertainment and management of COVID-19 pneumonia in the UK: Insights from high-incidence regions. Lancet Resp Med.

    Google Scholar 

  20. Liu, K.-C., et al. (2020). CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity. European Journal of Radiology, 126, 108941.

    Article  Google Scholar 

  21. Zhou, Z., Guo, D., Li, C., et al. (2020). Coronavirus disease 2019: Initial chest CT findings. European Radiology, 1(1), 1–19.

    Google Scholar 

  22. Yoon, S. H., et al. (2020). Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): Analysis of nine patients treated in Korea. Korean Journal of Radiology, 21(4), 494–500. https://doi.org/10.3348/kjr.2020.0132

    Article  Google Scholar 

  23. Goodfellow, I. J. (2014). On distinguishability criteria for estimating generative models. arXiv, 1(1), 1–6.

    Google Scholar 

  24. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv.

    Google Scholar 

  25. Zimmerer, D., Kohl, S. A. A., Petersen, J., Isensee, F., & Maier-Hein, K. H. (2018). Context-encoding variational autoencoder for unsupervised anomaly detection. arXiv.

    Google Scholar 

  26. Chen, X., & Konukoglu, E.. (2018). Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv.

    Google Scholar 

  27. Pawlowski, N., Lee, M. C. H., Rajchl, M., McDonagh, S., Ferrante, E., Kamnitsas, K., Cooke, S., Stevenson, S., Khetani, A., Newman, T., et al. (2018) Unsupervised lesion detection in brain CT using Bayesian convolutional autoencoders.

    Google Scholar 

  28. Zhang, J., Xie, Y., Liao, Z., Pang, G., Verjans, J., Li, W., Sun, Z., He, J., & Li, C. S. Y. (2020). Viral pneumonia screening on chest x-ray images using confidence-aware anomaly detection. arXiv.

    Google Scholar 

  29. de Moura, J., Novo, J., & Ortega, M. (2020). Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images. medRxiv.

    Google Scholar 

  30. Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., & Singh, S. (2020). Deep transfer learning based classification model for COVID-19 disease. Irbm, 1, 1–6.

    Google Scholar 

  31. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M. P., & Ng, A. Y. (2017). CheXNet: RAdiologist-level pneumonia detection on chest X-rays with deep learning. ArXiv, 3–9.

    Google Scholar 

  32. Verma, D., Bose, C., Tufchi, N., Pant, K., Tripathi, V., & Thapliyal, A. (2020). An efficient framework for identification of tuberculosis and pneumonia in chest X-ray images using neural network. Procedia Computer Science, 171(1), 217–224.

    Google Scholar 

  33. Zhang, Y., Niu, S., Qiu, Z., Wei, Y., Zhao, P., Yao, J., Huang, J., Wu, Q., & Tan, M. (2020). COVID-DA: Deep domain adaptation from typical pneumonia to COVID-19, XX, 2020, pp. 1–8.

    Google Scholar 

  34. Butt, C., Gill, J., Chun, D., & Babu, B. A. (2020). Deep learning system to screen coronavirus disease 2019 pneumonia. Applied Intelligence, 2019, 1–29.

    Google Scholar 

  35. Mangal, A., Kalia, S., Rajgopal, H., Rangarajan, K., Namboodiri, V., Banerjee, S., & Arora, C. (2020). CovidAID: COVID-19 detection using chest X-ray. ArXiv.

    Google Scholar 

  36. Gabruseva, T., Poplavskiy, D., & Kalinin, A. A. (2020). Deep learning for automatic pneumonia detection, 2019.

    Google Scholar 

  37. Mohammed, I., Singh, N., Area, B., & National, L.B. (n.d.). Computer-assisted detection and diagnosis of pediatric pneumonia in chest X-ray images, 1(1), 1–9.

    Google Scholar 

  38. de Moraes Batista, A. F., Miraglia, J. L., Donato, T. H. R., & Filho, A. D. P. C. (2020). Covid-19 diagnosis prediction in emergency care patients: a machine learning approach. medRxiv, 1(1), 1–13.

    Google Scholar 

  39. Jenifer, S., Parasuraman, S., & Kadirvelu, A. (2016). Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrastlimited adaptive histogram equalization algorithm. Applied Soft Computing, 42, 167–177.

    Article  Google Scholar 

  40. Schwab, P., Schütte, A. D. M., Dietz, B., & Bauer, S. (2020). predcovid-19: A systematic study of clinical predictive models for coronavirus disease 2019. arXiv.

    Google Scholar 

  41. Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., et al. (2020). Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. CMC: Computers, Materials & Continua, 63, 537–551.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

    Bhimavarapu Usharani

Authors
  1. Bhimavarapu Usharani
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science and Engg., Orissa Engineering College, Bhubaneswar-Khordha, Odisha, India

    Subhendu Kumar Pani

  2. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University (Erstwhile North Orissa University), Takatpur, Baripada, Odisha, India

    Sujata Dash

  3. Biomedical Engineering, Federal University of Pernambuco, Recife, Pernambuco, Brazil

    Wellington P. dos Santos

  4. Comp Science, Mathematics and Science, St. John’s University, Jamaica, NY, USA

    Syed Ahmad Chan Bukhari

  5. Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden

    Francesco Flammini

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Usharani, B. (2022). COVID-19 Detection Using Discrete Particle Swarm Optimization Clustering with Image Processing. In: Pani, S.K., Dash, S., dos Santos, W.P., Chan Bukhari, S.A., Flammini, F. (eds) Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-79753-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79753-9_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79752-2

  • Online ISBN: 978-3-030-79753-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation