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

Shabut, Antesar M. and Hoque Tania, Marzia and Lwin, Khin T. and Evans, Benjamin A. and Yusof, Nor Azah and Abu-Hassan, Kamal J. and Hossain, Alamgir (2018) An Intelligent Mobile-Enabled Expert System for Tuberculosis Disease Diagnosis in Real Time. Expert Systems with Applications, 114. pp. 65-77. ISSN 0957-4174

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Official URL: https://doi.org/10.1016/j.eswa.2018.07.014

Abstract

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.

Item Type: Journal Article
Keywords: Image processing, Machine learning, Decision support system, Colourimetric tests
Faculty: Faculty of Science & Technology
Depositing User: Dr Antesar Shabut
Date Deposited: 11 Jul 2018 15:50
Last Modified: 07 Jul 2019 01:02
URI: http://arro.anglia.ac.uk/id/eprint/703380

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