An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

•Investigation into the development of a multi-disciplinary diagnosis framework for Tuberculosis.•Tuberculosis-specific antibody detection in real time using mobile phone.•Exploration of image processing technique to analyse contents of plasmonic ELISA without experts.•Enhancement of detection accur...

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Veröffentlicht in:Expert systems with applications 2018-12, Vol.114, p.65-77
Hauptverfasser: Shabut, Antesar M., Hoque Tania, Marzia, Lwin, Khin T., Evans, Benjamin A., Yusof, Nor Azah, Abu-Hassan, Kamal J., Hossain, M.A.
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container_end_page 77
container_issue
container_start_page 65
container_title Expert systems with applications
container_volume 114
creator Shabut, Antesar M.
Hoque Tania, Marzia
Lwin, Khin T.
Evans, Benjamin A.
Yusof, Nor Azah
Abu-Hassan, Kamal J.
Hossain, M.A.
description •Investigation into the development of a multi-disciplinary diagnosis framework for Tuberculosis.•Tuberculosis-specific antibody detection in real time using mobile phone.•Exploration of image processing technique to analyse contents of plasmonic ELISA without experts.•Enhancement of detection accuracy using mobile enabled expert system up to 98.4%. 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 resource-intensive 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.
doi_str_mv 10.1016/j.eswa.2018.07.014
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subjects Antibodies
Artificial intelligence
Colourimetric tests
Decision support system
Decision support systems
Diagnosis
Diagnostic tests
Expert systems
Image processing
Image processing systems
Machine learning
Medical imaging
Real time
Tuberculosis
title An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time
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