An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images
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School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA
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Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Jackson State University, Jackson, MS 39213, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Margherita Fanelli and Giuseppe Banfi
Int. J. Environ. Res. Public Health 2022, 19(4), 2013; https://doi.org/10.3390/ijerph19042013
Received: 10 December 2021
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Revised: 25 January 2022
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Accepted: 9 February 2022
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Published: 11 February 2022
(This article belongs to the Special Issue COVID-19 in North America: Clinical, Psychosocial, Laboratory, and Epidemiological Aspects)
The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19.
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Keywords:
COVID-19; SARS-CoV-2; chest X-ray; Deep Learning Model
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MDPI and ACS Style
Chakraborty, S.; Murali, B.; Mitra, A.K. An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images. Int. J. Environ. Res. Public Health 2022, 19, 2013. https://doi.org/10.3390/ijerph19042013
AMA Style
Chakraborty S, Murali B, Mitra AK. An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images. International Journal of Environmental Research and Public Health. 2022; 19(4):2013. https://doi.org/10.3390/ijerph19042013
Chicago/Turabian StyleChakraborty, Somenath, Beddhu Murali, and Amal K. Mitra. 2022. "An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images" International Journal of Environmental Research and Public Health 19, no. 4: 2013. https://doi.org/10.3390/ijerph19042013
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