Automatic recognition of melanoma using Support Vector Machines: A study based on Wavelet, Curvelet and color features

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

Automatic recognition of melanoma using Support Vector Machines: A study based on Wavelet, Curvelet and color features

Takruri, M

Al-Jumaily, A

Mahmoud, MKA

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Industrial Automation, Information and Communications Technology, IAICT 2014, 2014, pp. 70 - 75
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Takruri, M https://orcid.org/0000-0001-9785-3920	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Mahmoud, MKA	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Industrial Automation, Information and Communications Technology, IAICT 2014, 2014, pp. 70 - 75	en_US
dc.identifier.isbn	9781479949090	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30466
dc.description.abstract	© 2014 IEEE. This paper proposes an automated non-invasive system for skin cancer (melanoma) detection based on Support Vector Machine classification. The proposed system uses a number of features extracted from the Wavelet or the Curvelet decomposition of the grayscale skin lesion images and color features obtained from the original color images. The dataset used include both digital images and Dermoscopy images for skin lesions that are either benign or malignant. The recognition accuracy obtained by the Support Vector Machine classifier used in this experiment is 87.7.1% for the Wavelet based features and 83.6. 6% for the Curvelet based ones. The proposed system also resulted in a sensitivity of 86.4 % for the case of Wavelet and 76.9% for the case of Curvelet. It also resulted in a specificity of 88.1% for the case of Wavelet and 85.4% for the case of Curvelet. The obtained sensitivity and specificity results are comparable to those obtained by Dermatologists.	en_US
dc.relation.ispartof	Proceedings - International Conference on Industrial Automation, Information and Communications Technology, IAICT 2014	en_US
dc.relation.isbasedon	10.1109/IAICT.2014.6922110	en_US
dc.title	Automatic recognition of melanoma using Support Vector Machines: A study based on Wavelet, Curvelet and color features	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Bali, Indonesia
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:

© 2014 IEEE. This paper proposes an automated non-invasive system for skin cancer (melanoma) detection based on Support Vector Machine classification. The proposed system uses a number of features extracted from the Wavelet or the Curvelet decomposition of the grayscale skin lesion images and color features obtained from the original color images. The dataset used include both digital images and Dermoscopy images for skin lesions that are either benign or malignant. The recognition accuracy obtained by the Support Vector Machine classifier used in this experiment is 87.7.1% for the Wavelet based features and 83.6. 6% for the Curvelet based ones. The proposed system also resulted in a sensitivity of 86.4 % for the case of Wavelet and 76.9% for the case of Curvelet. It also resulted in a specificity of 88.1% for the case of Wavelet and 85.4% for the case of Curvelet. The obtained sensitivity and specificity results are comparable to those obtained by Dermatologists.

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http://hdl.handle.net/10453/30466