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

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

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

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

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 5th International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 236 - 241
Issue Date:: 2013-01

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Field	Value	Language
dc.contributor.author	Abu Mahmoud, M	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	Eduard babulak, SKKU	en_US
dc.date	2013-09-24	en_US
dc.date.issued	2013-01	en_US
dc.identifier.citation	Proceedings of the 5th International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 236 - 241	en_US
dc.identifier.isbn	978-1-4799-2308-3	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27982
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 filter for filtering and a Kmeans clustering for segmentation. The extracted features were reduced by using sequential feature selection and then classified 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 classification accuracy of 88.9%, sensitivity of 87.5% and specificity of 100%.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of the 5th International Conference on Computational Intelligence, Modelling and Simulation	en_US
dc.relation.ispartof	International Conference on Computational Intelligence, Modelling and Simulation	en_US
dc.relation.isbasedon	10.1109/CIMSim.2013.45	en_US
dc.rights	© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	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	Piscataway, USA	en_US
utslib.location.activity	Seoul, South Korea	en_US
utslib.for	080106 Image Processing	en_US
dc.location.activity	Seoul, South Korea	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
pubs.organisational-group	/University of Technology Sydney/Strength - GEVI - Green Energy and Vehicle Innovations
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Piscataway, USA	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 filter for filtering and a Kmeans clustering for segmentation. The extracted features were reduced by using sequential feature selection and then classified 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 classification accuracy of 88.9%, sensitivity of 87.5% and specificity of 100%.

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http://hdl.handle.net/10453/27982