IDEAS home Printed from https://ideas.repec.org/a/aif/journl/v5y2021i3p27-41.html
   My bibliography  Save this article

Medical Text Classification Based on Convolutional Neural Network: A Review

Author

Listed:
  • Hazha Saeed Yahia

    (Poly Techniques University, Duhok, Kurdistan Region, Iraq)

  • Adnan Mohsin Abdulazeez

    (Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq)

Abstract

Medical text classification has a significant impact on disease diagnosis, medical research, and the automatic development of disease ontology, acquiring knowledge of clinical results recorded in the medical literature. Hence, medical text classification is challenging because it contains terminologies that describe medical concepts and terminologies. Furthermore, the medical data mostly does not follow natural language grammar; it has inadequate grammatical sentences. The techniques used for text classification give different results comparing to medical text classifications, as extracting text and training sets are different. One of the most significant text classification models in general and medical text classification specifically is CNN-based models. In this paper, many papers on medical text classification have been reviewed, and the details of each article, such as algorithms, or approaches used, databases, classification techniques, and outcomes obtained, are evaluated and outlined thoroughly. Besides, discussions were carried out on all the studied papers, which profoundly influenced medical documents classification.

Suggested Citation

  • Hazha Saeed Yahia & Adnan Mohsin Abdulazeez, 2021. "Medical Text Classification Based on Convolutional Neural Network: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 27-41.
  • Handle: RePEc:aif:journl:v:5:y:2021:i:3:p:27-41
    as

    Download full text from publisher

    File URL: https://ijsab.com/wp-content/uploads/683.pdf
    Download Restriction: no

    File URL: https://ijsab.com/volume-5-issue-3/3593
    Download Restriction: no
    ---><---

    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:aif:journl:v:5:y:2021:i:3:p:27-41. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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: Farjana Rahman (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.