IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v13y2024i2p412-419.html
   My bibliography  Save this article

A scoping review of literature on deep learning and symbolic AI-based framework for detecting Covid-19 using computerized tomography scans

Author

Listed:
  • Vengai Musanga

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, 4000, South Africa)

  • Colin Chibaya

    (School of Natural and Applied Sciences, Sol Plaatje University, Kimberly, South Africa)

  • Serestina Viriri

    (School of Mathematics, University of KwaZulu-Natal, Durban, 4000, South Africa)

Abstract

This scoping review aims to explore various Deep Learning and Symbolic Artificial Intelligence (AI) models that can be integrated into explainable hybrid AI for the purpose of detecting COVID-19 based on Computerized Tomography (CT) scans. We followed the PRISMA-ScR framework as the foundation for our scoping review protocol. Our approach included a thorough search across 13 databases, complemented by an additional random internet search for relevant articles. Due to the voluminous number of articles returned, the search was further narrowed using the keywords: Deep Learning, Symbolic AI and Hybrid AI. These keywords were used because they are more visible in the earmarked literature. A screening of all articles by title was performed to remove duplicates. The final screening process centered on the publication year, ensuring that all considered articles fell within the range of 2019 to 2023, inclusive. Subsequently, abstract or text synthesis was conducted. Our search query retrieved a total of 3,312 potential articles from the thirteen databases, and an additional 12 articles from a random internet search, resulting in a cumulative count of 3,324 identified articles. After the deduplication and screening steps, 260 articles met our inclusion criteria. These articles were categorized based on the year of publication, the type of aim, and the type of AI used. An analysis of the year of publication revealed a linear trend, indicating growth in the field of Hybrid AI. Out of the five aim categories identified, we deliberately excluded articles that lacked a specified aim. It's noteworthy that 3% of the articles focused on the integration of AI models. The low percentage value suggests that the integration aspect is overlooked, thereby transcripting the integration of Deep Learning and symbolic AI into hybrid AI as an area worth exploring. This scoping review gives an overview of how a Deep Learning and Symbolic AI-based framework has been used in the detection of COVID-19 based on CT scans. Key Words:Deep Learning, Symbolic AI, Hybrid AI, Scoping Review

Suggested Citation

  • Vengai Musanga & Colin Chibaya & Serestina Viriri, 2024. "A scoping review of literature on deep learning and symbolic AI-based framework for detecting Covid-19 using computerized tomography scans," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 13(2), pages 412-419, March.
    • Handle: RePEc:rbs:ijbrss:v:13:y:2024:i:2:p:412-419
    DOI: 10.20525/ijrbs.v13i2.2955
    as

    Download full text from publisher

    File URL: https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/2955/2260
    Download Restriction: no
    File URL: https://doi.org/10.20525/ijrbs.v13i2.2955
    Download Restriction: no
    File URL: https://libkey.io/10.20525/ijrbs.v13i2.2955?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
    ---><---

    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:rbs:ijbrss:v:13:y:2024:i:2:p:412-419. 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: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    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.