IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v16y2024i1p101-112.html
   My bibliography  Save this article

An empirical approach towards detection of tuberculosis using deep convolutional neural network

Author

Listed:
  • Syed Azeem Inam
  • Daniyal Iqbal
  • Hassan Hashim
  • Mansoor Ahmed Khuhro

Abstract

Tuberculosis remains among the top disease, causing death all over the globe and its timely detection is a major concern for medical practitioners, especially after the emergence of the SARS-CoV-2 pandemic. Even with the recent advances in the methods for medical image classification, it is still challenging to diagnose tuberculosis without considering the associated historical and biological factors. There has been a great contribution of unsupervised learning in the development of techniques for image classification and the present study has utilised a deep convolutional neural network for detecting tuberculosis. It proposes a network comprising 54 layers having 59 connections. After computations, our proposed deep convolutional neural network attained an accuracy of 99.79%, 99.46%, and 99.5% for the classes of healthy, sick, and tuberculosis (TB) respectively for a public dataset, achieving higher accuracy as compared to other pre-trained network models.

Suggested Citation

  • Syed Azeem Inam & Daniyal Iqbal & Hassan Hashim & Mansoor Ahmed Khuhro, 2024. "An empirical approach towards detection of tuberculosis using deep convolutional neural network," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 16(1), pages 101-112.
    • Handle: RePEc:ids:ijdmmm:v:16:y:2024:i:1:p:101-112
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=136232
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    More about this item

    Keywords

    tuberculosis; image classification; deep convolutional neural network; DCNN; accuracy; F1 score.;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade

    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:ids:ijdmmm:v:16:y:2024:i:1:p:101-112. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    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.