IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v142y2021ics0960077920308870.html
   My bibliography  Save this article

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images

Author

Listed:
  • Hussain, Emtiaz
  • Hasan, Mahmudul
  • Rahman, Md Anisur
  • Lee, Ickjai
  • Tamanna, Tasmi
  • Parvez, Mohammad Zavid

Abstract

The Coronavirus 2019, or shortly COVID-19, is a viral disease that causes serious pneumonia and impacts our different body parts from mild to severe depending on patient’s immune system. This infection was first reported in Wuhan city of China in December 2019, and afterward, it became a global pandemic spreading rapidly around the world. As the virus spreads through human to human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Preventing viral spreading requires early detection of positive cases and to treat infected patients as quickly as possible. The need for COVID-19 testing kits has increased, and many of the developing countries in the world are facing a shortage of testing kits as new cases are increasing day by day. In this situation, the recent research using radiology imaging (such as X-ray and CT scan) techniques can be proven helpful to detect COVID-19 as X-ray and CT scan images provide important information about the disease caused by COVID-19 virus. The latest data mining and machine learning techniques such as Convolutional Neural Network (CNN) can be applied along with X-ray and CT scan images of the lungs for the accurate and rapid detection of the disease, assisting in mitigating the problem of scarcity of testing kits.

Suggested Citation

  • Hussain, Emtiaz & Hasan, Mahmudul & Rahman, Md Anisur & Lee, Ickjai & Tamanna, Tasmi & Parvez, Mohammad Zavid, 2021. "CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
  • Handle: RePEc:eee:chsofr:v:142:y:2021:i:c:s0960077920308870
    DOI: 10.1016/j.chaos.2020.110495
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920308870
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2020.110495?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wu, Gang-Zhou & Fang, Yin & Kudryashov, Nikolay A. & Wang, Yue-Yue & Dai, Chao-Qing, 2022. "Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Giorgio Ciano & Paolo Andreini & Tommaso Mazzierli & Monica Bianchini & Franco Scarselli, 2021. "A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation," Mathematics, MDPI, vol. 9(22), pages 1-16, November.
    3. Kiran Fatima & Habiba Azam & Fiaz Ahmad Sulehri & Syeda Ambreen Fatima Bukhari & Hafiz Khalique Ur Rehman Virk & Yunjiang Geng & Marc Audi & Muhammad Saleem Ashraf, 2024. "Sustainability Disclosures and Their Influence on Cost of Capital: A Comprehensive Bibliometric Study," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(2), pages 799-810.

    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:eee:chsofr:v:142:y:2021:i:c:s0960077920308870. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.