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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)

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

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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Cite This Article

A. Majid, M. Attique 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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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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