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

Research output: Contribution to journalArticle

  • Antesar Shabut
  • Marzia Hoque Tania
  • Khin T. Lwin
  • Benjamin A. Evans
  • Nor Azah Yusof
  • Kamal J. Abu-Hassan
  • M.A. Hossain
Original languageEnglish
Pages (from-to)65-77
Number of pages13
JournalExpert Systems with Applications
Early online date7 Jul 2018
StatePublished - 30 Dec 2018
Externally publishedYes
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.

    Research areas

  • Image processing, Machine learning, Decision support system, Colourimetric tests

External organisations

  • Anglia Ruskin University
  • University of East Anglia
  • Universiti Putra Malaysia
  • University of Bath

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