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An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images

Author

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  • Somenath Chakraborty

    (School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA)

  • Beddhu Murali

    (School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA)

  • Amal K. Mitra

    (Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Jackson State University, Jackson, MS 39213, USA)

Abstract

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.

Suggested Citation

  • Somenath Chakraborty & Beddhu Murali & Amal K. Mitra, 2022. "An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2013-:d:746853
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    References listed on IDEAS

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    2. Altan, Aytaç & Karasu, Seçkin, 2020. "Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Fan Wu & Su Zhao & Bin Yu & Yan-Mei Chen & Wen Wang & Zhi-Gang Song & Yi Hu & Zhao-Wu Tao & Jun-Hua Tian & Yuan-Yuan Pei & Ming-Li Yuan & Yu-Ling Zhang & Fa-Hui Dai & Yi Liu & Qi-Min Wang & Jiao-Jiao , 2020. "A new coronavirus associated with human respiratory disease in China," Nature, Nature, vol. 579(7798), pages 265-269, March.
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    Cited by:

    1. Nitin Arora & Aditya Kakde & Subhash C. Sharma, 2023. "An optimal approach for content-based image retrieval using deep learning on COVID-19 and pneumonia X-ray Images," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 246-255, March.

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