Vol.69, No.1, 2021, pp.319-337, doi:10.32604/cmc.2021.016816
COVID19 Classification Using CT Images via Ensembles of Deep Learning Models
  • Abdul Majid1, Muhammad Attique Khan1, Yunyoung Nam2,*, Usman Tariq3, Sudipta Roy4, Reham R. Mostafa5, Rasha H. Sakr6
1 Department of Computer Science, HITEC University Taxila, Taxila, 47080, Pakistan
2 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
3 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
4 PRTTL, Washington University in Saint Louis, Saint Louis, MO 63110, USA
5 Department of Information Systems, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt
6 Department of Computer Science, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt
* Corresponding Author: Yunyoung Nam. Email:
(This article belongs to this Special Issue: Retrospective Big Data Analytics in Radiological Imaging for Precision Medicine)
Received 12 January 2021; Accepted 20 March 2021; Issue published 04 June 2021
The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the choice of key features. Here, we propose a set of deep learning features based on a system for automated classification of computed tomography (CT) images to identify COVID-19. Initially, this method was used to prepare a database of three classes: Pneumonia, COVID-19, and Healthy. The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach. In the next step, two advanced deep learning models (ResNet50 and DarkNet53) were fine-tuned and trained through transfer learning. The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach. For each deep model, the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach. Later, the selected features were merged using the new minimum parallel distance non-redundant (PMDNR) approach. The final fused vector was finally classified using the extreme machine classifier. The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%. Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.
COVID19; preprocessing; deep learning; information fusion; firefly algorithm; extreme learning machine
Cite This Article
A. Majid, M. A. Khan, Y. Nam, U. Tariq, S. Roy et al., "Covid19 classification using ct images via ensembles of deep learning models," Computers, Materials & Continua, vol. 69, no.1, pp. 319–337, 2021.
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