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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/21399

Title: Implementation of an improved cellular neural network algorithm for brain tumor detection
Authors: Azian Azamimi, Abdullah
Bu, Sze Chize
Nishio, Yoshifumi
???metadata.dc.contributor.url???: azamimi@unimap.edu.my
nishio@unimap.edu.my
Keywords: Brain tumor;Magnetic Resonance Imaging (MRI) images;cellular neural networks (CNNs);Templates;Image processing
Issue Date: 27-Feb-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: p. 611-615
Series/Report no.: Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012)
Abstract: Image processing plays an important role in medical diagnosis. In this paper, a brain tumor detection method based on cellular neural networks (CNNs) is proposed. Brain tumor is an abnormal growth of cells inside the skull. To examine the location of tumor in the brain, Magnetic Resonance Imaging (MRI) is used. Radiologists will evaluate the grey scale MRI images. This procedure is really time and energy consuming. To overcome this problem, an automated detection method for brain tumor using CNN is developed. By using the template in the CNN simulator, output of the desired image can be performed. Therefore, many templates were combined in order to obtain an accurate result that will help radiologists detecting the tumor in brain images easily.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178990
http://hdl.handle.net/123456789/21399
ISBN: 978-145771989-9
Appears in Collections:Azian Azamimi Abdullah
Conference Papers

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