Classification of Malignant Melanoma and Benign Nevi from Skin Lesions Based on Support Vector Machine

Abu Mahmoud, MK; Al-Jumaily, A; Maali, Y; Anam, K

Classification of Malignant Melanoma and Benign Nevi from Skin Lesions Based on Support Vector Machine

Abu Mahmoud, MK Al-Jumaily, A Maali, Y Anam, K

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Publisher:: CIMSim2013
Publication Type:: Conference Proceeding
Citation:: 2013, pp. 236 - 241 (6)
Issue Date:: 2013

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Field	Value	Language
dc.contributor.author	Abu Mahmoud, MK	en_US
dc.contributor.author	Al-Jumaily, A	en_US
dc.contributor.author	Maali, Y	en_US
dc.contributor.author	Anam, K	en_US
dc.contributor.editor	CIMSim2013	en_US
dc.date	2013-09-24	en_US
dc.date.issued	2013	en_US
dc.identifier.citation	2013, pp. 236 - 241 (6)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35018
dc.description.abstract	This paper proposes an automated system for discrimination between melanocytic nevi and malignant melanoma. The proposed system used a number of features extracted from histo-pathological images of skin lesions through image processing techniques which consisted of a spatially adaptive color median lter for ltering and a Kmeans clustering for segmentation. The extracted features were reduced by using sequential feature selection and then classied by using support vector machine (SVM) to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The proposed system was able to achieve a good result with classication accuracy of 88.9%, sensitivity of 87.5% and specicity of 100%.	en_US
dc.publisher	CIMSim2013	en_US
dc.relation.ispartof	IEEE 5th International conference on Computational Intelligence, Modelling and Simulation	en_US
dc.relation.isbasedon	10.1109/cimsim.2013.45	en_US
dc.title	Classification of Malignant Melanoma and Benign Nevi from Skin Lesions Based on Support Vector Machine	en_US
dc.type	Conference Proceeding
utslib.location.activity	South Korea	en_US
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	South Korea
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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - GEVI - Green Energy and Vehicle Innovations
utslib.copyright.status	closed_access
pubs.declined	1970-01-01T00:00:00.0+1000
pubs.consider-herdc	false	en_US
pubs.deleted	1970-01-01T00:00:00.0+1000
pubs.finish-date	2013-09-26	en_US
pubs.start-date	2013-09-24	en_US

Abstract:

This paper proposes an automated system for discrimination between melanocytic nevi and malignant melanoma. The proposed system used a number of features extracted from histo-pathological images of skin lesions through image processing techniques which consisted of a spatially adaptive color median lter for ltering and a Kmeans clustering for segmentation. The extracted features were reduced by using sequential feature selection and then classied by using support vector machine (SVM) to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The proposed system was able to achieve a good result with classication accuracy of 88.9%, sensitivity of 87.5% and specicity of 100%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/35018