The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification

Mahmoud, MKA; Al-Jumaily, A; Takruri, M

The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification

Mahmoud, MKA Al-Jumaily, A

Takruri, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011, 2011, pp. 680 - 685
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Mahmoud, MKA	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Takruri, M	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011, 2011, pp. 680 - 685	en_US
dc.identifier.isbn	9781457721502	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19117
dc.description.abstract	This paper proposes an automatic skin cancer (melanoma) classification system. The input for the prosed system is a collected data images, it followed by different image processing procedures to enhance the image properties. Two segmentation methods used to identify the normal skin cancer from malignant skin and to extract the useful information from these images that passed to the classifier for training and testing. The features used for classification is the coefficients created by Wavelet decompositions and simple wrapper curvelet. Curvelet is suitable for the image that contains oriented texture and cartoon edges. Recognition accuracy of the three layers back-propagation neural network classifier with wavelet is 51.1% and with curvelet is 75. 6% in digital images database. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011	en_US
dc.relation.isbasedon	10.1109/HIS.2011.6122188	en_US
dc.title	The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
dc.location.activity	Malacca, Malaysia	en_US
dc.location.activity	Brussels, BELGIUM
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	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper proposes an automatic skin cancer (melanoma) classification system. The input for the prosed system is a collected data images, it followed by different image processing procedures to enhance the image properties. Two segmentation methods used to identify the normal skin cancer from malignant skin and to extract the useful information from these images that passed to the classifier for training and testing. The features used for classification is the coefficients created by Wavelet decompositions and simple wrapper curvelet. Curvelet is suitable for the image that contains oriented texture and cartoon edges. Recognition accuracy of the three layers back-propagation neural network classifier with wavelet is 51.1% and with curvelet is 75. 6% in digital images database. © 2011 IEEE.

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http://hdl.handle.net/10453/19117