PSO-SVM hybrid system for melanoma detection from histo-pathological images

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

PSO-SVM hybrid system for melanoma detection from histo-pathological images

Takruri, M Mahmoud, MKA Al-Jumaily, A

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Publisher:: Institute of Advanced Engineering and Science
Publication Type:: Journal Article
Citation:: International Journal of Electrical and Computer Engineering, 2019, 9, (4), pp. 2941-2949
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Takruri, M
dc.contributor.author	Mahmoud, MKA
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463
dc.date.accessioned	2020-05-11T05:54:33Z
dc.date.available	2020-05-11T05:54:33Z
dc.date.issued	2019-01-01
dc.identifier.citation	International Journal of Electrical and Computer Engineering, 2019, 9, (4), pp. 2941-2949
dc.identifier.issn	2088-8708
dc.identifier.issn	2088-8708
dc.identifier.uri	http://hdl.handle.net/10453/140631
dc.description.abstract	Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved. This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%. The sensitivity and specificity results are comparable to those obtained by dermatologists.
dc.language	en
dc.publisher	Institute of Advanced Engineering and Science
dc.relation.ispartof	International Journal of Electrical and Computer Engineering
dc.relation.isbasedon	10.11591/ijece.v9i4.pp2941-2949
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	PSO-SVM hybrid system for melanoma detection from histo-pathological images
dc.type	Journal Article
utslib.citation.volume	9
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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-11T05:54:31Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	9
utslib.start-page	2941
utslib.citation.issue	4

Abstract:

Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved. This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%. The sensitivity and specificity results are comparable to those obtained by dermatologists.

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http://hdl.handle.net/10453/140631