COVID-19 Disease Detection Based on Machine Learning and Chest X-Ray Images


  • Ramyar Abdulrahman Teimoor Department of Computer, College of Science, University of Sulaimani, Iraq
  • Mihran A. Muhammed Department of Computer, College of Science, University of Sulaimani, Iraq



COVID-19, Convolutional Neural Networks, Residual Network 50, Visual Geometry Group 19, Visual Geometry Group 16, X-ray Image, Machine Learning


Due to increasing population, automated illness identification has become a critical problem in medical research. An automated illness detection framework aids physicians in disease diagnosis by providing precise, consistent, and quick findings, as well as lowering the mortality rate. Coronavirus (COVID-19) has expanded worldwide and is now one of the most severe and acute disorders. To avoid COVID-19 from spreading, making an automatic detection system based on X-ray chest pictures ought to be the quickest diagnostic alternative. The goal of this research is to come up with the best model for detecting COVID-19 diagnosis with the greatest accuracy. Therefore, four models, Convolutional Neural Networks, Residual Network 50, Visual Geometry Group 16 (VGG16), and VGG19, have been evaluated using the same images preprocessing method. In this study, performance metrics include accuracy, precision, recall, and F1 scores are used for evaluating proposed method. According to our findings, the VGG16 model is a viable candidate for detecting COVID-19 instances, because it has highest accuracy; in result overall accuracy of 98.44% in training phase, 98.05% invalidation phase and 96.05% in testing phase is obtained. The results of other performance measurements are shown in the result section, demonstrating that the majority of the approaches are more than 90% accurate. Based on these results, radiologists may find the proposed VGG16 model to be an intriguing and a helpful tool for detecting and diagnosing COVID-19 patients quickly.


A. Rehman, T. Saba, U. Tariq and N. Ayesha. “Deep learning-based COVID-19 detection using CT and X-ray images: Current analytics and comparisons”. IT Professional, vol. 23, no. 3, pp. 63- 68, 2021.

M. Maia, J. S. Pimentel, I. S. Pereira, J. Gondim, M. E. Barreto and A. Ara. “Convolutional support vector models: Prediction of coronavirus disease using chest x-rays”. Information, vol. 11, no. 12, pp. 1-19, 2020.

K. B. Prakash, S. S. Imambi, M. Ismail, T. P. Kumar and Y. V. R. Pawan. “Analysis, prediction and evaluation of COVID-19 datasets”. International Journal of Emerging Trends in Engineering Research, vol. 8, no. 5, pp. 2199-2204, 2020.

M. Z. Islam, M. M. Islam and A. Asraf. “A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images”. Informatics in Medicine Unlocked, vol. 20, p. 100412, 2020.

R. A. Teimoor and A. M. Darwesh. “Node detection and tracking in smart cities based on internet of things and machine learning”. UHD Journal of Science and Technology, vol. 3, no. 1, pp. 30-38, 2019.

K. Ahammed, M. S. Satu, M. Z. Abedin, M. A. Rahaman and S. M. S. Islam. “Early detection of coronavirus cases using chest X-ray images employing machine learning and deep learning approaches”. medRxiv, Vol. 2, p. 2020.06.07.20124594, 2020.

F. Saiz and I. Barandiaran. “COVID-19 detection in chest X-ray images using a deep learning approach”. International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 2, p. 4, 2020.

A. M. Ismael and A. Şengür. “Deep learning approaches for COVID-19 detection based on chest X-ray images”. Expert Systems with Applications, vol. 164, p. 114054, 2021.

T. Zebin and S. Rezvy. “COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization”. Applied Intelligence, vol. 51, no. 2, pp. 1010-1021, 2021.

L. Gaur, U. Bhatia, N. Z. Jhanjhi, G. Muhammad and M. Masud. Medical image-based detection of COVID-19 using deep convolution neural networks. Multimedia Systems, vol. 27, pp. 1-10, 2021.

C. Sitaula and M. B. Hossain. “Attention-based VGG-16 model for COVID-19 chest X-ray image classification”. vol. 19, pp. 2850- 2863, 2021.

“COVID-19 Radiography Database.” Available from: https://www.

M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, et al., “Can AI Help in Screening Viral and COVID-19 Pneumonia?,” IEEE Access, vol. 8, pp. 132665- 132676, 2020.

R. Mohammadi, M. Salehi, H. Ghaffari and A. A. Rohani. “JBPE_ Volume 10_Issue 5_Pages 559-568.pdf”. vol. 2019, pp. 559-568, 2020.

K. Simonyan and A. Zisserman. “Very deep convolutional networks for large-scale image recognition”. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1-14, 2015.

H. Swapnarekha, H. S. Behera, D. Roy, S. Das and J. Nayak. “Competitive deep learning methods for COVID-19 detection using X-ray images”. Journal of The Institution of Engineers (India): Series B, 2021.

C. Ouchicha, O. Ammor and M. Meknassi. “CVDNet: A novel deep learning architecture for detection of coronavirus (COVID-19) from chest x-ray images”. Chaos, Solitons and Fractals, vol. 140, 2020.

J. E. Luján-García, M. A. Moreno-Ibarra, Y. Villuendas-Rey and C. Yáñez-Márquez. “Fast COVID-19 and pneumonia classification using chest X-ray images”. Mathematics, vol. 8, no. 9, 2020.