A hybrid system for skin lesion detection: Based on gabor wavelet and support vector machine

Mahmoud, MKA; Al-Jumaily, A

A hybrid system for skin lesion detection: Based on gabor wavelet and support vector machine

Mahmoud, MKA Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2014 International Symposium on Information Technology, ISIT 2014, 2015, pp. 39 - 44
Issue Date:: 2015-01-01

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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.date.issued	2015-01-01	en_US
dc.identifier.citation	Proceedings of the 2014 International Symposium on Information Technology, ISIT 2014, 2015, pp. 39 - 44	en_US
dc.identifier.isbn	9781138027855	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30481
dc.description.abstract	© 2015 Taylor & Francis Group, London. Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extraction from histo-pathological images and subsequent classification is presented. The proposed automated system uses a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. Then, a wavelet approach is used to extract the features; more specifically Wavelet Packet Transform (WPT). This article introduces a novel melanoma detection strategy using a hybrid particle swarm - based support vector machine (SVM–WLG–PSO) technique. The extracted features are reduced by using a particle swarm optimization (PSO), this was used to optimize the SVM parameters as a feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve one of the best results with classification accuracy of 87.13%, sensitivity of 94.1% and specificity of 80.22%.	en_US
dc.relation.ispartof	Proceedings of the 2014 International Symposium on Information Technology, ISIT 2014	en_US
dc.title	A hybrid system for skin lesion detection: Based on gabor wavelet and support vector machine	en_US
dc.type	Conference Proceeding
dc.location.activity	Australia
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:

© 2015 Taylor & Francis Group, London. Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extraction from histo-pathological images and subsequent classification is presented. The proposed automated system uses a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. Then, a wavelet approach is used to extract the features; more specifically Wavelet Packet Transform (WPT). This article introduces a novel melanoma detection strategy using a hybrid particle swarm - based support vector machine (SVM–WLG–PSO) technique. The extracted features are reduced by using a particle swarm optimization (PSO), this was used to optimize the SVM parameters as a feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve one of the best results with classification accuracy of 87.13%, sensitivity of 94.1% and specificity of 80.22%.

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http://hdl.handle.net/10453/30481