SA-SVM based automated diagnostic System for Skin Cancer

Masood, A; Al-Jumaily, A

SA-SVM based automated diagnostic System for Skin Cancer

Masood, A Al-Jumaily, A

Permalink

Publication Type:: Conference Proceeding
Citation:: 2015
Issue Date:: 2015-03-04

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (845.49 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Masood, A	en_US
dc.contributor.author	Al-Jumaily, A	en_US
dc.contributor.editor	Wang	en_US
dc.contributor.editor	Jiang	en_US
dc.contributor.editor	Zhang	en_US
dc.date	2015-09-17	en_US
dc.date.issued	2015-03-04	en_US
dc.identifier.citation	2015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35515
dc.description.abstract	Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. The underlying features which indicate the difference between melanoma and benign lesions are obtained through intensity, spatial/frequency and texture based methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The presented work also considers analyzed performance of linear and kernel based SVM on the specific skin lesion diagnostic problem and discussed corresponding findings. The best diagnostic rates obtained through the proposed method are around 90.5 %.	en_US
dc.relation.ispartof	Sixth International Conference on Graphic and Image Processing (ICGIP 2014)	en_US
dc.relation.isbasedon	10.1117/12.2179094	en_US
dc.title	SA-SVM based automated diagnostic System for Skin Cancer	en_US
dc.type	Conference Proceeding
utslib.location.activity	Paris	en_US
dc.location.activity	Paris
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 Elec, Mech and Mechatronic Systems
pubs.organisational-group	/University of Technology Sydney/Strength - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.declined	1970-01-01T00:00:00.0+1000
pubs.consider-herdc	true	en_US
pubs.deleted	1970-01-01T00:00:00.0+1000
pubs.finish-date	2014-05-19	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2015-09-17	en_US

Abstract:

Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. The underlying features which indicate the difference between melanoma and benign lesions are obtained through intensity, spatial/frequency and texture based methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The presented work also considers analyzed performance of linear and kernel based SVM on the specific skin lesion diagnostic problem and discussed corresponding findings. The best diagnostic rates obtained through the proposed method are around 90.5 %.

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

http://hdl.handle.net/10453/35515