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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/18750

Title: Development of cellular neural network algorithm for detecting lung cancer symptoms
Authors: Azian Azamimi, Abdullah
Hasdiana, Mohamaddiah
???metadata.dc.contributor.url???: azamimi@unimap.edu.my
s061150177@unimap.edu.my
Keywords: Lung cancer;Cellular neural networks;X-ray films;Image processing
Issue Date: 30-Nov-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: p. 138-143
Series/Report no.: Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
Abstract: Lung cancer is the most common of lethal types of cancer. One of the most important and difficult tasks a doctor has to carry out is the detection and diagnosis of cancerous lung nodules from x-ray image's result. Some of these lesions may not be detected because of camouflaged by the underlying anatomical structure, the low-quality of the images or the subjective and variable decision criteria used by doctors. Hence, a detection system using cellular neural network (CNN) is developed in order to help the doctors to recognize the doubtful lung cancer regions in x-ray films. In this study, a CNN algorithm for detecting the boundary and area of lung cancer in x-ray image has been proposed. Computer simulation result shows that our CNN algorithm is verified to detect some key lung cancer symptoms successfully and has been proved by radiologist.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5742216
http://hdl.handle.net/123456789/18750
ISBN: 978-1-4244-7600-8
Appears in Collections:Azian Azamimi Abdullah
Conference Papers

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