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An Automated Intelligent Identification and Counting System Procedure for Tuberculosis

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Main Authors: Fatin Atiqah, Rosli, Mohd Yusoff, Mashor, Siti Suraya, Md Noor
Other Authors: yusoff@unimap.edu.my
Format: Article
Language:English
Published: IOP Publishing 2020
Subjects:
Mycobacterium Tuberculosis
Tuberculosis (TB)
Intelligent identification
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69010
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spelling my.unimap-690102020-12-16T02:31:35Z An Automated Intelligent Identification and Counting System Procedure for Tuberculosis Fatin Atiqah, Rosli Mohd Yusoff, Mashor Siti Suraya, Md Noor yusoff@unimap.edu.my Mycobacterium Tuberculosis Tuberculosis (TB) Intelligent identification Link to publisher's homepage at https://iopscience.iop.org/ Tuberculosis (TB) is an infectious disease caused by Mycobacterium Tuberculosis or TB Bacilli. Currently, the classification of TB bacilli is carried out by microbiologist by using Ziehl-Nielsen (ZN) stained smear sputum slide under a light microscopy. However, the manual evaluation is time-consuming and lead to slow decision. Furthermore, the sensitivity is less due to incline of human error which lead to inaccurate conclusion. Therefore, this study proposes an intelligence identification and counting system to detect the presence of TB bacilli in the ZN-stained smear sputum image. This system is designed to identify the presence of TB bacilli and count the number of TB bacilli by applying digital image processing and artificial intelligence techniques. In image acquisition, there are 70 samples images of ZN-stained smear sputum image were collected from Hospital Universiti Sains Malaysia (HUSM) Kubang Kerian, Kota Bharu, Kelantan, Malaysia. The image processing technique consists of contrast enhancement, segmentation, and feature extraction. The contrast of original image was enhanced by the combination of global enhancement, local enhancement and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, the enhanced image was segmented using color thresholding and the features were extracted consists of on 18 colour features, 15 shape features and 5 texture features. Afterward, the features underwent feature selection to select the relevant features by using Neighborhood Component Analysis (NCA) and ReliefF Analysis. The study showed that there are relevant features were chosen by ReliefF at feature weight more than 0.004 including (8 colour features, 11 shape feature and 3 texture features) for improving the performance and accuracy of Multilayer Perceptron (MLP) trained by Scaled Conjugate Gradient (SCG). For classification process, MLP, k-Nearest Neighborhood (k-NN) and Support Vector Machine (SVM) are used with 6 folds cross-validation. It was found that MLP has the highest of accuracy, sensitivity and specificity with 93.8%, 93.4% and 94.1% respectively. 2020-12-16T02:31:35Z 2020-12-16T02:31:35Z 2019 Article Journal of Physics: Conference Series, vol.1372, 2019, 6 pages 1742-6588 (print) 1742-6596 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69010 https://iopscience.iop.org/ en International Conference on Biomedical Engineering (ICoBE); IOP Publishing
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Mycobacterium Tuberculosis
Tuberculosis (TB)
Intelligent identification
spellingShingle Mycobacterium Tuberculosis
Tuberculosis (TB)
Intelligent identification
Fatin Atiqah, Rosli
Mohd Yusoff, Mashor
Siti Suraya, Md Noor
An Automated Intelligent Identification and Counting System Procedure for Tuberculosis
description Link to publisher's homepage at https://iopscience.iop.org/
author2 yusoff@unimap.edu.my
author_facet yusoff@unimap.edu.my
Fatin Atiqah, Rosli
Mohd Yusoff, Mashor
Siti Suraya, Md Noor
format Article
author Fatin Atiqah, Rosli
Mohd Yusoff, Mashor
Siti Suraya, Md Noor
author_sort Fatin Atiqah, Rosli
title An Automated Intelligent Identification and Counting System Procedure for Tuberculosis
title_short An Automated Intelligent Identification and Counting System Procedure for Tuberculosis
title_full An Automated Intelligent Identification and Counting System Procedure for Tuberculosis
title_fullStr An Automated Intelligent Identification and Counting System Procedure for Tuberculosis
title_full_unstemmed An Automated Intelligent Identification and Counting System Procedure for Tuberculosis
title_sort automated intelligent identification and counting system procedure for tuberculosis
publisher IOP Publishing
publishDate 2020
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69010
_version_ 1698698543243460608
score 13.222552

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