Automatically early detection of skin cancer: Study based on nueral netwok classification

Lau, HT; Al-Jumaily, A

Automatically early detection of skin cancer: Study based on nueral netwok classification

Lau, HT Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: SoCPaR 2009 - Soft Computing and Pattern Recognition, 2009, pp. 375 - 380
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Lau, HT	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	SoCPaR 2009 - Soft Computing and Pattern Recognition, 2009, pp. 375 - 380	en_US
dc.identifier.isbn	9780769538792	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10878
dc.description.abstract	In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo. © 2009 IEEE.	en_US
dc.relation.ispartof	SoCPaR 2009 - Soft Computing and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/SoCPaR.2009.80	en_US
dc.title	Automatically early detection of skin cancer: Study based on nueral netwok classification	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Malaysia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo. © 2009 IEEE.

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http://hdl.handle.net/10453/10878