Utilizing support vector machines for the characterization of digital dermatological images

Maglogiannis, I; Zafiropoulos, EP

dc.contributor.author	Maglogiannis, I	en
dc.contributor.author	Zafiropoulos, EP	en
dc.date.accessioned	2014-03-01T02:49:37Z
dc.date.available	2014-03-01T02:49:37Z
dc.date.issued	2003	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34640
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-1642394255&partnerID=40&md5=e2cffc5be222f9b460a79817a347a471	en
dc.subject	Image analysis	en
dc.subject	Image classification	en
dc.subject	Malignant melanoma	en
dc.subject	Support vector machines	en
dc.subject.other	Algorithms	en
dc.subject.other	Dermatology	en
dc.subject.other	Feature extraction	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Skin	en
dc.subject.other	Statistical methods	en
dc.subject.other	Discriminant analysis	en
dc.subject.other	Dysplastic nevus	en
dc.subject.other	Image classification	en
dc.subject.other	Malignant melanoma	en
dc.subject.other	Skin lesions	en
dc.subject.other	Support vector machines	en
dc.subject.other	Image processing	en
dc.title	Utilizing support vector machines for the characterization of digital dermatological images	en
heal.type	conferenceItem	en
heal.publicationDate	2003	en
heal.abstract	In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions. The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic nevus. Border and color based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Although larger sample sizes are necessary to resolve these issues fully, the support vector machines algorithm performed excellently achieving a high percentage of correct classification (approximately 100%). Two other classification methods, the statistical discriminant analysis and the application of neural networks were applied also to the same problem and their efficiency was compared with the support vector machines algorithm performance.	en
heal.journalName	Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications	en
dc.identifier.volume	3	en
dc.identifier.spage	1293	en
dc.identifier.epage	1297	en