Staff View: Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen

Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen

Tuberculosis (TB) is a highly infectious disease. TB diagnosis is usually done manually by microbiologist through microscopic examination of sputum specimen of TB patients for pulmonary TB diseases. However, this practice is time consuming and labour-intensive. Hence, it results in fatigue and work...

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Main Author:	Rafikha Aliana, A. Raof
Other Authors:	Prof. Dr. Mohd. Yusoff Mashor
Format:	Thesis
Language:	English
Published:	Universiti Malaysia Perlis (UniMAP) 2016
Subjects:	Tuberculosis (TB) Infectious disease Tuberculosis Diagnostic System Sputum specimen Tuberculosis detection
Online Access:	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41291
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id	my.unimap-41291
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spelling	my.unimap-412912016-04-13T04:36:20Z Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen Rafikha Aliana, A. Raof Prof. Dr. Mohd. Yusoff Mashor Tuberculosis (TB) Infectious disease Tuberculosis Diagnostic System Sputum specimen Tuberculosis detection Tuberculosis (TB) is a highly infectious disease. TB diagnosis is usually done manually by microbiologist through microscopic examination of sputum specimen of TB patients for pulmonary TB diseases. However, this practice is time consuming and labour-intensive. Hence, it results in fatigue and work overload to the microbiologists, thus reduces the diagnostic performance. This research involved in the development of automated intelligent diagnosis system for tuberculosis detection based on Ziehl-Neelsen sputum specimen. The system developed is also equipped with automatic capturing system for capturing sputum slide images automatically using 40X lens. Besides that, this study also suggested the combination of image processing technique with artificial neural network in creating a new procedure for diagnosing process of Ziehl-Neelsen sputum specimen. Image enhancement technique based on white balance and partial contrast method has been proposed. A new procedure for segmentation technique was also proposed based on the combination of kmeans clustering, 3 × 3 median filter and automated seed based region growing algorithm. The study also includes feature extraction where features such as size, colour and shape were chosen in classifying TB bacilli with the aid of artificial neural network. This research proposed to use HMLP network with MRPE algorithm for detection and classification of TB bacilli. The system is supposed to reduce the problems arise during the diagnosis of tuberculosis disease such as avoidance of eye fatigue to the microbiologist due to observing through the microscope eyepiece for a long period of time. It has been shown that the classification for sputum slide specimen for TB diagnosis produces good results with classification accuracy of more than 94%. These findings suggest the potential use of this software in diagnosing pulmonary TB disease. The conducted research has provided the platform for automated intelligent system to diagnose tuberculosis disease. 2016-04-13T04:36:20Z 2016-04-13T04:36:20Z 2014 Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41291 en Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
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	Tuberculosis (TB) Infectious disease Tuberculosis Diagnostic System Sputum specimen Tuberculosis detection
spellingShingle	Tuberculosis (TB) Infectious disease Tuberculosis Diagnostic System Sputum specimen Tuberculosis detection Rafikha Aliana, A. Raof Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
description	Tuberculosis (TB) is a highly infectious disease. TB diagnosis is usually done manually by microbiologist through microscopic examination of sputum specimen of TB patients for pulmonary TB diseases. However, this practice is time consuming and labour-intensive. Hence, it results in fatigue and work overload to the microbiologists, thus reduces the diagnostic performance. This research involved in the development of automated intelligent diagnosis system for tuberculosis detection based on Ziehl-Neelsen sputum specimen. The system developed is also equipped with automatic capturing system for capturing sputum slide images automatically using 40X lens. Besides that, this study also suggested the combination of image processing technique with artificial neural network in creating a new procedure for diagnosing process of Ziehl-Neelsen sputum specimen. Image enhancement technique based on white balance and partial contrast method has been proposed. A new procedure for segmentation technique was also proposed based on the combination of kmeans clustering, 3 × 3 median filter and automated seed based region growing algorithm. The study also includes feature extraction where features such as size, colour and shape were chosen in classifying TB bacilli with the aid of artificial neural network. This research proposed to use HMLP network with MRPE algorithm for detection and classification of TB bacilli. The system is supposed to reduce the problems arise during the diagnosis of tuberculosis disease such as avoidance of eye fatigue to the microbiologist due to observing through the microscope eyepiece for a long period of time. It has been shown that the classification for sputum slide specimen for TB diagnosis produces good results with classification accuracy of more than 94%. These findings suggest the potential use of this software in diagnosing pulmonary TB disease. The conducted research has provided the platform for automated intelligent system to diagnose tuberculosis disease.
author2	Prof. Dr. Mohd. Yusoff Mashor
author_facet	Prof. Dr. Mohd. Yusoff Mashor Rafikha Aliana, A. Raof
format	Thesis
author	Rafikha Aliana, A. Raof
author_sort	Rafikha Aliana, A. Raof
title	Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
title_short	Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
title_full	Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
title_fullStr	Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
title_full_unstemmed	Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
title_sort	development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen
publisher	Universiti Malaysia Perlis (UniMAP)
publishDate	2016
url	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41291
_version_	1643799627678351360
score	13.211869

Development of an automated intelligent diagnostic system for tuberculosis detection based on sputum specimen

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