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Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue

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Main Authors: Muhammad Khusairi, Osman, Mohd Yusoff, Mashor, Prof. Dr., Hasnan, Jaafar, Assoc. Prof. Dr.
Other Authors: khusairi@ppinang.uitm.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Biomedical image processing
Mycobacterium tuberculosis detection
Neural networks
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/20842
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spelling my.unimap-208422012-09-05T14:06:40Z Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr. Hasnan, Jaafar, Assoc. Prof. Dr. khusairi@ppinang.uitm.edu.my Biomedical image processing Mycobacterium tuberculosis detection Neural networks Link to publisher's homepage at http://ieeexplore.ieee.org/ The application of image processing and artificial intelligence for computer-aided tuberculosis (TB) diagnosis has received considerable attention over the past several years and still is an active research area. Several approaches have been proposed to improve the diagnostic performance in term of diagnostic accuracy and processing efficiency. This paper studies the performance of a recent training algorithm called Online Sequential Extreme Learning Machine (OS-ELM) for detection and classification of TB bacilli in tissue specimens. The algorithm is used to train a single hidden layer feedforward network (SLFN) using a set of data consists of simple geometrical features, such as area, perimeter, eccentricity and shape factor as feature vectors. All of these features are extracted from tissue images which consist of TB bacilli and further classified into three types; TB, overlapped TB and non-TB. Promising result with 91.33% of testing accuracy has been achieved for the OS-ELM using sigmoid activation function and 40-by-40 learning mode. 2012-09-05T14:06:40Z 2012-09-05T14:06:40Z 2012-02-27 Working Paper p. 139-143 978-145771989-9 http://ezproxy.unimap.edu.my:2080/stamp/stamp.jsp?tp=&arnumber=6178971 http://hdl.handle.net/123456789/20842 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
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 Biomedical image processing
Mycobacterium tuberculosis detection
Neural networks
spellingShingle Biomedical image processing
Mycobacterium tuberculosis detection
Neural networks
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar, Assoc. Prof. Dr.
Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 khusairi@ppinang.uitm.edu.my
author_facet khusairi@ppinang.uitm.edu.my
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar, Assoc. Prof. Dr.
format Working Paper
author Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar, Assoc. Prof. Dr.
author_sort Muhammad Khusairi, Osman
title Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
title_short Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
title_full Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
title_fullStr Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
title_full_unstemmed Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
title_sort online sequential extreme learning machine for classification of mycobacterium tuberculosis in ziehl-neelsen stained tissue
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/20842
_version_ 1643793195388108800
score 13.214268

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