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Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification

Overview of attention for article published in Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 2017
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Title
Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification
Published in
Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 2017
DOI 10.1109/embc.2017.8037859
Pubmed ID
Authors

AbuHassan, Kamal J, Bakhori, Noremylia M, Kusnin, Norzila, Azmi, Umi Z M, Tania, Marzia H, Evans, Benjamin A, Yusof, Nor A, Hossain, M A

Abstract

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Student > Master 1 25%
Student > Bachelor 1 25%
Readers by discipline Count As %
Computer Science 1 25%
Psychology 1 25%
Chemistry 1 25%
Engineering 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 October 2017.
All research outputs
#10,686,764
of 12,050,803 outputs
Outputs from Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society
#1,416
of 1,893 outputs
Outputs of similar age
#238,858
of 284,726 outputs
Outputs of similar age from Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society
#3
of 3 outputs
Altmetric has tracked 12,050,803 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,893 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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