Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification
| Autor: | Benjamin A. Evans, Norzila Kusnin, Noremylia Mohd Bakhori, Mohammed Alamgir Hossain, Kamal Abuhassan, Umi Zulaikha Mohd Azmi, Nor Azah Yusof, Marzia Hoque Tania |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Tuberculosis
Color Enzyme-Linked Immunosorbent Assay Expert Systems Computational intelligence 02 engineering and technology computer.software_genre Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Humans Medicine Cluster analysis Contextual image classification business.industry Pattern recognition Image segmentation 021001 nanoscience & nanotechnology medicine.disease Thresholding Expert system Data set 020201 artificial intelligence & image processing Artificial intelligence 0210 nano-technology business computer Biomedical engineering |
| Zdroj: | EMBC |
| DOI: | 10.1109/embc.2017.8037859 |
| Popis: | Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|