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An Intelligent Mobile-Enabled Expert System for Tuberculosis Disease Diagnosis in Real Time

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posted on 2023-08-30, 15:28 authored by Antesar M. Shabut, Marzia Hoque Tania, Khin T. Lwin, Benjamin A. Evans, Nor Azah Yusof, Kamal J. Abu-Hassan, Mohammed Alamgir Hossain
This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resourceintensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.

History

Refereed

  • Yes

Volume

114

Page range

65-77

Publication title

Expert Systems with Applications

ISSN

0957-4174

Publisher

Elsevier

File version

  • Accepted version

Language

  • eng

Legacy posted date

2018-07-11

Legacy creation date

2018-07-06

Legacy Faculty/School/Department

ARCHIVED Faculty of Science & Technology (until September 2018)

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