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
Format:	Thesis
Language:	English
Subjects:	Tuberculosis (TB) Infectious disease Tuberculosis Diagnostic System Sputum specimen Tuberculosis detection
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/41291/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/41291/2/Full%20text.pdf
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id	my-unimap-41291
record_format	uketd_dc
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) 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. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41291 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/41291/1/Page%201-24.pdf 1a0b5e5ffb305476098cd84b5a96f54d http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/41291/2/Full%20text.pdf 2e748161bbaabf1364075a789cfb8b99 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/41291/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Tuberculosis (TB) Infectious disease Tuberculosis Diagnostic System Sputum specimen Tuberculosis detection School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Prof. Dr. Mohd. Yusoff Mashor
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.
format	Thesis
author	Rafikha Aliana, A. Raof
author_facet	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
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/41291/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/41291/2/Full%20text.pdf
_version_	1747836812281249792

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

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