An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time
| Autor: | Mohammed Alamgir Hossain, Antesar M. Shabut, Nor Azah Yusof, Kamal Abuhassan, Khin T. Lwin, Marzia Hoque Tania, Benjamin A. Evans |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Decision support system
Tuberculosis Computer science Image processing 02 engineering and technology computer.software_genre Machine learning Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Tuberculosis Disease biology business.industry General Engineering 020206 networking & telecommunications medicine.disease Expert system Computer Science Applications Random forest Binary classification biology.protein 020201 artificial intelligence & image processing Artificial intelligence Antibody business computer Classifier (UML) |
| ISSN: | 0957-4174 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect