17
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

      research-article
      a , a , * , b , a , c , d , a , e , f , g
      Journal of X-Ray Science and Technology
      IOS Press
      COVID-19, Chest X-Ray Image, transfer learning, image identification

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          BACKGROUND:

          The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system.

          OBJECTIVE:

          One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images.

          METHODS:

          Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task.

          RESULTS:

          A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively.

          CONCLUSION:

          This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

          Related collections

          Most cited references72

          • Record: found
          • Abstract: found
          • Article: found

          Detection of SARS-CoV-2 in Different Types of Clinical Specimens

          This study describes results of PCR and viral RNA testing for SARS-CoV-2 in bronchoalveolar fluid, sputum, feces, blood, and urine specimens from patients with COVID-19 infection in China to identify possible means of non-respiratory transmission.
            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            Going deeper with convolutions

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              A Survey on Transfer Learning

                Bookmark

                Author and article information

                Journal
                J Xray Sci Technol
                J Xray Sci Technol
                XST
                Journal of X-Ray Science and Technology
                IOS Press (Nieuwe Hemweg 6B, 1013 BG Amsterdam, The Netherlands )
                0895-3996
                1095-9114
                31 July 2020
                19 September 2020
                2020
                : 28
                : 5
                : 821-839
                Affiliations
                [a ]Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University , Shenyang, China
                [b ]Department of Electrical and Computer Engineering, Stevens Institute of Technology , Hoboken, NJ, USA
                [c ]Faculty of Biochemistry and Molecular Medicine, University of Oulu , Oulu, Finland
                [d ]Liaoning Hospital and Institute, Cancer Hospital of China Medical University , Shenyang, China
                [e ]Electrical Engineering Department, Pratt School of Engineering Duke University , Durham, NC, USA
                [f ]Whiting School of Engineering, Johns Hopkins University , 500 W University Parkway, MD, USA, USA
                [g ]Environmental Engineering Department, Northeastern University , Shenyang, China
                Author notes
                [* ]Corresponding author: Chen Li, Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819 China. E-amil: lichen201096@ 123456hotmail.com .
                Article
                XST200715
                10.3233/XST-200715
                7592691
                32773400
                a4f06b99-61e7-433c-b128-fbea5b0b2237
                © 2020 – IOS Press and the authors. All rights reserved

                This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 May 2020
                : 29 June 2020
                : 11 July 2020
                Categories
                Research Article

                covid-19,chest x-ray image,transfer learning,image identification

                Comments

                Comment on this article