Helping Healthcare Providers to Differentiate COVID-19 Pneumonia by Analyzing Digital Chest X-Rays: Role of Artificial Intelligence in Healthcare Practice

Abu Naser Zafar Ullah, Md. Habibur Rahman, Shaikh Muhammad Allayear, Mohammed Liakwat Ali Khan, Sheikh Md. Faysal, ABM Alauddin Chowdhury, Md. Nasir Uddin, Hafiz T. A. Khan

International Journal of Biomedicine. 2022;12(3):459-465.
DOI: 10.21103/Article12(3)_OA21
Originally published September 5, 2022


Background: Detecting COVID-19 pneumonia and differentiating it from community acquired pneumonia (CAP) has been a challenging task for healthcare providers since the pandemic began. We therefore aim to develop and evaluate a simple, non-invasive tool to accurately detect COVID-19 by using digital chest X-ray (CXR).
Methods and Results: We performed a retrospective, multi-center study in which deep learning frameworks were used to develop the system architecture of the diagnostic tool. The tool was trained and validated by using data from the GitHub database and two hospitals in Bangladesh. Python programming was used to calculate all statistical estimates. Our study revealed that the artificial intelligence (AI)-based diagnostic tool was able to detect COVID-19 accurately by examining chest X-ray (CXR). During the testing phase, the tool could interpret CXR with precision of 0.98, recall/sensitivity of 0.97 and F1 score of 0.97 for COVID-19. The evaluation results showed high sensitivity (90%) and specificity (92%) in detecting COVID-19. The AUC values for COVID-19 and pneumonia were 0.91 and 0.87, respectively.
Conclusion: The developed AI-based diagnostic tool can offer the healthcare providers an effective means of detecting and differentiating COVID-19 from other types of pneumonia, thus contributing to reducing the long-term impact of this deadly disease
Key Points:

  • The major strength of this study is that it has led to the development of a technology-based tool that can precisely detect COVID-19 at an early stage and immediately isolate infected patients from the healthy population.
  • This next generation test can be accessed by healthcare providers remotely and therefore provide an added convenience.
  • The main limitation is with this tool COVID-19 pneumonia may be confused with other types of pneumonia if the quality of the chest image is too low. Moreover, it focuses only on detecting whether or not the disease is COVID-19, but not its severity.
COVID – 19 • chest X-Ray • artificial intelligence • Bangladesh
  1. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19); WHO: Geneva, Switzerland, 2020.
  2. WHO Director-General's opening remarks at the media briefing on COVID–19 (2020).
  3. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020 Mar 26;382(13):1199-1207. doi: 10.1056/NEJMoa2001316. 
  4. Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. 2020 Feb 15;395(10223):470-473. doi: 10.1016/S0140-6736(20)30185-9. Epub 2020 Jan 24. Erratum in: Lancet. 2020 Jan 29. 
  5. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al.; China Novel Coronavirus Investigating and Research Team. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020 Feb 20;382(8):727-733. doi: 10.1056/NEJMoa2001017. 
  6. Zhang R, Tie X, Qi Z, Bevins NB, Zhang C, Griner D, et al. Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence. Radiology. 2021 Feb;298(2):E88-E97. doi: 10.1148/radiol.2020202944. 
  7. Udugama B, Kadhiresan P, Kozlowski HN, Malekjahani A, Osborne M, Li VYC, Chen H, Mubareka S, Gubbay JB, Chan WCW. Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano. 2020 Apr 28;14(4):3822-3835. doi: 10.1021/acsnano.0c02624. 
  8. Peng M, Yang J, Shi Q, Ying L, Zhu H, Zhu G, et al. Artificial Intelligence Application in COVID-19 Diagnosis and Prediction (2/17/2020). Available at SSRN:
  9. Qin ZZ, Sander MS, Rai B, Titahong CN, Sudrungrot S, Laah SN, Adhikari LM, Carter EJ, Puri L, Codlin AJ, Creswell J. Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep. 2019 Oct 18;9(1):15000. doi: 10.1038/s41598-019-51503-3. 
  10. GitHub database. Available at:
  11. Guo Y, Liu Y, Oerlemans A. Deep learning for visual understanding: A review. Neurocomputing. 2016;187:27-48.
  12. Arel I, Rose DC, Karnowski TP. Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]. IEEE Computational Intelligence Magazine. 2010;5(4):13-18. doi: 10.1109/MCI.2010.938364
  13. Shakirov VV, Solovyeva KP, Dunin-Barkowski WL. Review of State-of-the-Art in Deep Learning Artificial Intelligence. Opt Mem Neural Networks.2018;27:65–80. doi: 10.3103/S1060992X18020066
  14. Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis.2015;115:211–252
  15. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Visual Recognition Available at:
  16. LI L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology. 2020; Mar 19: 200905. doi: 10.1148/radiol.2020200905
  17. Chen X, Tang Y, Mo Y, Li S, Lin D, Yang Z, Yang Z, Sun H, Qiu J, Liao Y, Xiao J, Chen X, Wu X, Wu R, Dai Z. A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study. Eur Radiol. 2020 Sep;30(9):4893-4902. doi: 10.1007/s00330-020-06829-2. 
  18. van Ginneken B. The Potential of Artificial Intelligence to Analyze Chest Radiographs for Signs of COVID-19 Pneumonia. Radiology. 2021 Apr;299(1):E214-E215. doi: 10.1148/radiol.2020204238. 

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Received May 10, 2022.
Accepted July 20, 2022.
©2022 International Medical Research and Development Corporation.