IDEAS home Printed from https://ideas.repec.org/a/igg/jehmc0/v13y2021i2p1-11.html
   My bibliography  Save this article

Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Author

Listed:
  • Malathy Jawahar

    (Central Leather Research Institute, Chennai, India)

  • L. Jani Anbarasi

    (Vellore Institute of Technology, Chennai, India)

  • Prassanna Jayachandran

    (Vellore Institute of Technology, Chennai, India)

  • Manikandan Ramachandran

    (SASTRA University, Thanjavur, India)

  • Fadi Al-Turjman

    (Near East University, Nicosia, Turkey)

Abstract

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

Suggested Citation

  • Malathy Jawahar & L. Jani Anbarasi & Prassanna Jayachandran & Manikandan Ramachandran & Fadi Al-Turjman, 2021. "Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(2), pages 1-11, July.
    • Handle: RePEc:igg:jehmc0:v:13:y:2021:i:2:p:1-11
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEHMC.20220701.oa2
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jehmc0:v:13:y:2021:i:2:p:1-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.