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Dias Júnior DA, da Cruz LB, Bandeira Diniz JO, et al. Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost. Expert Syst Appl. 2021;183:115452doi: 10.1016/j.eswa.2021.115452.
Dias Júnior, D. A., da Cruz, L. B., Bandeira Diniz, J. O., França da Silva, G. L., Junior, G. B., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M. (2021). Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost. Expert systems with applications, 183115452. https://doi.org/10.1016/j.eswa.2021.115452
Dias Júnior, Domingos Alves, et al. "Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost." Expert systems with applications vol. 183 (2021): 115452. doi: https://doi.org/10.1016/j.eswa.2021.115452
Dias Júnior DA, da Cruz LB, Bandeira Diniz JO, França da Silva GL, Junior GB, Silva AC, de Paiva AC, Nunes RA, Gattass M. Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost. Expert Syst Appl. 2021 Nov 30;183:115452. doi: 10.1016/j.eswa.2021.115452. Epub 2021 Jun 22. PMID: 34177133; PMCID: PMC8218245.
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Expert Syst Appl. 2021 Nov 30;183:115452. doi: 10.1016/j.eswa.2021.115452. Epub 2021 Jun 22.

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost.

Expert systems with applications

Domingos Alves Dias Júnior, Luana Batista da Cruz, João Otávio Bandeira Diniz, Giovanni Lucca França da Silva, Geraldo Braz Junior, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass

Affiliations

  1. Federal University of Maranhão Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil.
  2. Federal Institute of Maranhão BR-226, SN, Campus Grajaú, Vila Nova 65940-00, Grajaú, MA, Brazil.
  3. Rio de Janeiro State University, Boulevard 28 de Setembro, 77, Vila Isabel 20551-030, Rio de Janeiro, RJ, Brazil.
  4. Pontifical Catholic University of Rio de Janeiro, R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil.

PMID: 34177133 PMCID: PMC8218245 DOI: 10.1016/j.eswa.2021.115452

Abstract

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

© 2021 Elsevier Ltd. All rights reserved.

Keywords: COVID-19; Chest X-Rays; Deep features; Extreme gradient boosting; Medical images; Particle swarm optimization

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Comput Methods Programs Biomed. 2020 Oct;194:105532 - PubMed
  2. Phys Eng Sci Med. 2020 Jun;43(2):635-640 - PubMed
  3. N Engl J Med. 2020 Feb 20;382(8):727-733 - PubMed
  4. Comput Methods Programs Biomed. 2018 Dec;167:49-63 - PubMed
  5. JAMA Intern Med. 2020 Jul 1;180(7):927-928 - PubMed
  6. J Thorac Imaging. 2019 May;34(3):179-186 - PubMed
  7. Comput Biol Med. 2020 Aug;123:103906 - PubMed
  8. J Biomed Inform. 2001 Feb;34(1):4-14 - PubMed
  9. Acad Radiol. 2020 Apr;27(4):463-467 - PubMed
  10. Comput Methods Programs Biomed. 2019 Aug;177:285-296 - PubMed
  11. Pattern Anal Appl. 2021 May 9;:1-14 - PubMed
  12. Comput Methods Programs Biomed. 2020 Dec;197:105685 - PubMed
  13. Comput Methods Programs Biomed. 2018 Mar;156:191-207 - PubMed
  14. Nature. 2015 May 28;521(7553):436-44 - PubMed
  15. Comput Methods Programs Biomed. 2019 Mar;170:53-67 - PubMed
  16. Comput Biol Med. 2020 Jun;121:103792 - PubMed
  17. Inform Med Unlocked. 2020;19:100360 - PubMed
  18. J Autoimmun. 2020 May;109:102433 - PubMed
  19. Comput Methods Programs Biomed. 2018 Aug;162:109-118 - PubMed
  20. Clin Microbiol Infect. 2020 Jun;26(6):729-734 - PubMed
  21. Artif Intell Med. 2020 May;105:101845 - PubMed

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