Detection of Pneumonia in COVID-19 Patients Using X-ray Images

Year : 2023 | Volume : 01 | Issue : 01 | Page : 13-23

    S.S. Kiran

  1. K Gurucharan

  2. M. Rajan Babu

  3. S. Durgamadhuri

  4. M. Swathi

  5. M. Sravanthi

  1. Assistant Professor, Lendi Institute of Engineering and Technology, Andhra Pradesh, India
  2. Assistant Professor, Lendi Institute of Engineering and Technology, Andhra Pradesh, India
  3. Professor, Lendi Institute of Engineering and Technology, Andhra Pradesh, India
  4. Assistant Professor, Lendi Institute of Engineering and Technology, Andhra Pradesh, India
  5. Student, Lendi Institute of Engineering and Technology, Andhra Pradesh, India
  6. Student, Trainee Software Engineer, Prolifics, Telangana, India


This study explores the use of chest X-ray image analysis and deep learning methods to identify pneumonia in COVID-19 patients. Due to the pandemic, Proper as well as immediate examination of COVID-19 is now essential for patient care and disease control. This study proposes a novel approach that uses convolutional neural networks (CNNs) to automatically predict pneumonia in COVID-19 patients using chest X-ray images. In this study, an X-ray of the chest that have been categorized are one-hot encoded using machine learning techniques like LabelBinarizer and then converted into emphatic form applying the categorical functionality of Python. Following that, a method for identification is created utilizing a range of the deep learning features, including convolutional neural network (CNN), VGG16, average pooling 2D (AP2D), dropout, flatten, dense, and input. The proposed approach was evaluated on a 5000 chest X-ray images in the dataset, obtaining excellent classification accuracy of 94%, sensitivity of 60%, and specificity of 100% for healthy and infections with pneumonia. The results demonstrate The possibility of using deep learning methods with accurately and quickly identify pneumonia in COVID-19 patients, which can enhance the health of patients and contribute to preventing the disease’s spread. The method carefully reduces training loss while also improving accuracy.

Keywords: COVID-19, SARS-CoV-2, Pneumonia, CNN, One hot encoding, Deep Learning, X-Ray, Bacteria, Viruses, Medical imaging.

[This article belongs to International Journal of Radio Frequency Innovations(ijrfi)]

How to cite this article: S.S. Kiran, K Gurucharan, M. Rajan Babu, S. Durgamadhuri, M. Swathi, M. Sravanthi Detection of Pneumonia in COVID-19 Patients Using X-ray Images ijrfi 2023; 01:13-23
How to cite this URL: S.S. Kiran, K Gurucharan, M. Rajan Babu, S. Durgamadhuri, M. Swathi, M. Sravanthi Detection of Pneumonia in COVID-19 Patients Using X-ray Images ijrfi 2023 {cited 2023 Nov 17};01:13-23. Available from:

Browse Figures


  1. Gozes, M. Frid-Adar, H. Greenspan, P. D. Browning, H. Zhang et al., “Rapid Ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection patient monitoring using deep learning ct image analysis,” 2003,
  2. Worldometers,(2020).Coronavirus.: https://www.worldometers. info/coronavirus/?utm_campaign
  3. K. Bairagi, M. Masud, D. H. Kim et al., “Controlling the outbreak of COVID-19: a noncooperative game perspective,” IEEE Access, vol. 8, pp. 215570–215581, 2020.
  4. M. Hansell, A. A. Bankier, H. MacMahon, T. C. McLoud, N. L. Mu¨ller, and J. Remy, “Fleischner society: glossary of terms for thoracic imaging,” Radiology, vol. 246, no. 3, pp. 697–722, 2008.
  5. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using densenet201 based deep transfer learning,” Journal of Biomolecular Structure and Dynamics, vol. 23, pp. 1–8, 2020.
  6. Douarre, R. Schielein, C. Frindel, S. Gerth, and D. Rousseau, “Transfer learning from synthetic data applied to soil-root segmentation in X-ray tomography images,” Journalof Imaging, vol. 4, no. 5, p. 65, 2018.
  7. Zhang, G. Wang, M. Li, and S. Han, “Automated classification analysis of geological structures based on images data and deep learning model,” Applied Sciences, vol. 8, no. 12,p. 2493, 2018.
  8. Chen, Y. Zhang, C. Ouyang, F. Zhang, and J. Ma, “Automated landslides detection for mountain cities using multi-temporal remote sensing imagery,” Sensors, vol. 18, p. 821, 2018.
  9. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan, “Chest pathology detection using deeplearning with non-medical training,” in Proceedings of the 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 294– 297, New York, NY, USA, April 2015.
  10. I. Razzak, S. Naz, and A. Zaib, “Deep learning for medicalimage processing: overview, challenges and the future,” Lecture Notes in Computational Vision and Biomechanics, vol. 32, pp. 323–350, 2018.
  11. Narin, C. Kaya, and Z. Pamuk, “Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks,” 2020, 2003.10849.
  12. V. I. S. S. J. Szegedy, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826, Las Vegas, NV, USA, June 2016.
  13. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: a large-scale hierarchical image database,” in Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, Miami, FL, USA, June2009.
  14. Sharma, S. Rani, and D. Gupta, “Artificial intelligence-based classification of chest X-ray images into COVID-19 and other infectious diseases,” International Journal of Biomedical Imaging, vol. 2020, Article ID 8889023, 5 pages, 2020.
  15. Matsumoto T, Satoshi Kodera, Shinohara H, Issei Komuro. Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning [Internet]. ResearchGate. International Heart Journal Association; 2020 [cited 2023 Sep 22]. Available from:
  16. Li, Y. Zhuang, and S. X. Yang, “Cloud computing for big data processing,” Intelligent Automation & Soft Computing, vol. 23, no. 4, pp. 545-546, 2017.
  17. Grewal, M. M. Srivastava, P. Kumar, and S. Varadarajan,“Radnet: radiologist level accuracy using deep learning for hemorrhage detection in ct scans,” in Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 281–284, Washington, DC, USA, April 2018.
  18. Kaur, V. Kumar, V. Yadav, D. Singh, N. Kumar, and N. N. Das, “Metaheuristic-based deep COVID-19 screening model from chest X-ray images,” Journal of Healthcare Engineering, vol. 2020, Article ID 8829829, 17 pages, 2021.
  19. Gianchandani, A. Jaiswal, D. Singh et al., “Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images,” Journal of Ambient Intel-ligence and Humanized Computing, vol. 12, 2020.
  20. Narayan Das, N. Kumar, M. Kaur, V. Kumar, and D. Singh, “Automated deep transfer learning-based approach for de-tection of COVID-19 infection in chest X-rays,” in IRB-MElsevier, Amsterdam, Netherlands, 2020.
  21. Patel P. Chest X-ray (Covid-19 & Pneumonia) [Internet]. 2019 [cited 2023 Sep 22]. Available from:
  22. Missinglink,(2019). networkconcepts/convolutiona;-neural-network-build-one- keraspytorch/.
  23. H. Ayan, “Diagnosis of pneumonia from chest X-ray ¨ images using deep learning,” in Proceedings of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1–5, Istanbul, Turkey, April 2019.
  24. Brownlee, Difference between a Batch and an Epoch in a Neural Network, Machine Learning Mastery, San Francisco, CL, USA, 2021, difference-between-a-batch- and-an-epoch/.
  25. Towards data science, “&e most intuitive and eauest guide for CNN,” 2020, https://towardsdatascie guide-for-convolutional-neuralnetwork360 7be47480#:%7E:text=Flattening%20is%20converting%20the%20dat a,called%20a%20fully%2Dconnected%20layer
  26. Masud, G. S. Gaba, S. Alqahtani et al., “A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care,” IEEE Internet of 2ings Journal, vol. 12, 2021.
  27. Keras, “Pooling layers,” 2020, pooling_layers/average_pooling2d/.
  28. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. J. Soufi, “Deep-covid: predicting covid-19 from chest X-ray images using deep transfer learning,” 2020, 2004.09363.
  29. Misra, S. Jeon, S. Lee, R. Managuli, and C. Kim, “Multichannel transfer learning of chest X-ray images for screening of COVID-19,” 2020,
  30. Ucar and D. Korkmaz, “COVIDiagnosis-Net: deep bayessqueezenet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images,” Medical Hypotheses, vol. 140, Article ID 109761, 2020.
  31. M. A. Asmaa Abbas, “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network,” Artificial Intelligence Applications for COVID-19, Detection, Control, Prediction, and Diagnosis, vol. 33, 2020.
  32. Shorfuzzaman and M. Masud, “On the detection of covid19 from chest x-ray images using cnn-based transfer learning,” Computers, Materials & Continua, vol. 64, no. 3, pp. 1359–1381, 2020.
  33. D. Goodwin, C. Jaskolski, C. Zhong, and H. Asmani, “Intramodel variability in COVID-19 classification using chest X-ray images,” 2020,
  34. Karim, T. Dohmen, D. Rebholz-Schuhmann et al., ¨ “Deepcovidexplainer: explainable COVID-19 predictions based on chest X-Ray images,” 2020, 2004.04582.
  35. Zhang, S. Niu, Z. Qiu et al., “COVID-DA: deep domain adaptation from typical pneumonia to COVID-19,” 2020,
  36. Lv, W. Qi, Y. Li, L. Sun, and Y. Wang, “A cascade network for detecting COVID-19 using chest X-Rays,” 2020, https://

Regular Issue Subscription Original Research
Volume 01
Issue 01
Received September 8, 2023
Accepted September 22, 2023
Published November 17, 2023