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Preliminary study of pneumonia symptoms detection method using cellular neural network

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Main Authors: Azian Azamimi, Abdullah, Norafifah, Md Posdzi, Nishio, Yoshifumi
Other Authors: azamimi@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2011
Subjects:
Cellular Neural Network
CT Image
Image processing
Pneumonia symptoms
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/14063
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spelling my.unimap-140632011-10-08T02:49:42Z Preliminary study of pneumonia symptoms detection method using cellular neural network Azian Azamimi, Abdullah Norafifah, Md Posdzi Nishio, Yoshifumi azamimi@unimap.edu.my nishio@ee.tokushima-u.ac.jp Cellular Neural Network CT Image Image processing Pneumonia symptoms Link to publisher's homepage at http://ieeexplore.ieee.org/ Medical diagnosis is one of the most important procedure in which image processing are usefully applied. In this paper, a pneumonia symptoms detection method based on cellular neural networks (CNNs) is proposed. The CNN design is characterized by a virtual template expansion obtained through a multistep operation. It is based on linear space invariant 3 x 3 templates. The proposed design is capable of performing pneumonia symptoms detection within a short time. The main idea in Cellular Neural Network is that connection is allowed between adjacent units only. There are few rules in Cellular Neural Network that has to be implemented when designing the templates, such as state equation, output equation, boundary equation, and also the initial value. These templates are combined to create the most ideal algorithm to detect the pneumonia symptoms in an image. Candy software is used as a CNN simulator to detect the pneumonia symptoms area. It was tested on the 23 grayscale pneumonia symptoms CT image obtained from the diagnostic imaging department. The simulation results show good performance based on the difference grayscale color and segmentation between the normal area and lung region area. 2011-10-08T02:49:42Z 2011-10-08T02:49:42Z 2011-06-21 Working Paper p. 497-500 978-161284228-8 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5953933 http://hdl.handle.net/123456789/14063 en Proceedings of the 1st International Conference on Electrical, Control and Computer Engineering 2011 (InECCE 2011) 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 Cellular Neural Network
CT Image
Image processing
Pneumonia symptoms
spellingShingle Cellular Neural Network
CT Image
Image processing
Pneumonia symptoms
Azian Azamimi, Abdullah
Norafifah, Md Posdzi
Nishio, Yoshifumi
Preliminary study of pneumonia symptoms detection method using cellular neural network
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 azamimi@unimap.edu.my
author_facet azamimi@unimap.edu.my
Azian Azamimi, Abdullah
Norafifah, Md Posdzi
Nishio, Yoshifumi
format Working Paper
author Azian Azamimi, Abdullah
Norafifah, Md Posdzi
Nishio, Yoshifumi
author_sort Azian Azamimi, Abdullah
title Preliminary study of pneumonia symptoms detection method using cellular neural network
title_short Preliminary study of pneumonia symptoms detection method using cellular neural network
title_full Preliminary study of pneumonia symptoms detection method using cellular neural network
title_fullStr Preliminary study of pneumonia symptoms detection method using cellular neural network
title_full_unstemmed Preliminary study of pneumonia symptoms detection method using cellular neural network
title_sort preliminary study of pneumonia symptoms detection method using cellular neural network
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/14063
_version_ 1643791131813609472
score 13.222552

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