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On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning

Mohammad Shorfuzzaman1, *, Mehedi Masud1

1 College of Computers and Information Technology, Taif University, Taif, 21974, Saudi Arabia.

* Corresponding Author: Mohammad Shorfuzzaman. Email: email.

(This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)

Computers, Materials & Continua 2020, 64(3), 1359-1381.


Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights from some of the top layers. We also develop an ensemble model where the predictions from all chosen pre-trained models are combined to generate a single output. The models are evaluated through 5-fold cross validation using two publicly available data repositories containing healthy and infected (both COVID-19 and other pneumonia) chest X-ray images. We also leverage two different visualization techniques to observe how efficiently the models extract important features related to the detection of COVID- 19 patients. The models show high degree of accuracy, precision, and sensitivity. We believe that the models will aid medical professionals with improved and faster patient screening and pave a way to further COVID-19 research.


Cite This Article

M. Shorfuzzaman and M. Masud, "On the detection of covid-19 from chest x-ray images using cnn-based transfer learning," Computers, Materials & Continua, vol. 64, no.3, pp. 1359–1381, 2020.


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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