Open Access

ARTICLE

COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

by Abdul Majid¹, Muhammad Attique Khan¹, Yunyoung Nam^2,*, 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:

Computers, Materials & Continua 2021, 69(1), 319-337. https://doi.org/10.32604/cmc.2021.016816

Received 12 January 2021; Accepted 20 March 2021; Issue published 04 June 2021

Abstract

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.

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