Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue
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Institute of Electrical and Electronics Engineers (IEEE)
2012
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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) |
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Universiti Malaysia Perlis |
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Malaysia |
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Universiti Malaysia Perlis |
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| 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 |
13.222552 |