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Kernel methods for melanoma recognition
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2006 (English)In: Stud. Health Technol. Informatics, 2006, 983-988 p.Conference paper (Refereed)
Abstract [en]

Skin cancer is a spreading disease in the western world. Early detection and treatment are crucial for improving the patient survival rate. In this paper we present two algorithms for computer assisted diagnosis of melanomas. The first is the support vector machines algorithm, a state-of-the-art large margin classifier, which has shown remarkable performances on object recognition and categorization problems. The second method, spin glass-Markov random fields, combines results of statistical physics of spin glasses with Markov random fields. We compared the two approaches using color histograms as features. We benchmarked our methods with another algorithm presented in the literature, which uses a sophisticated segmentation technique and a set of features especially designed for melanoma recognition. To our knowledge, this algorithm represents the state of the art on skin lesions classification. We show with extensive experiments that the support vector machines approach outperforms the existing method and, on two classes out of three, it achieves performances comparable to those obtained by expert clinicians.

Place, publisher, year, edition, pages
2006. 983-988 p.
, Studies in Health Technology and Informatics, ISSN 0926-9630 ; 124
Keyword [en]
Computer Assisted Diagnosis, Kernel Methods, Melanoma Recognition, Support Vector Machines, algorithm, article, human, melanoma, methodology, Algorithms, Diagnosis, Computer-Assisted, Humans
National Category
Health Sciences
URN: urn:nbn:se:kth:diva-155336ISI: 000281143200140ScopusID: 2-s2.0-39049175775ISBN: 1586036475ISBN: 9781586036478OAI: diva2:763734
20th International Congress of the European Federation for Medical Informatics, MIE 2006, 27-30 August 2006, Maastricht, Netherlands

QC 20141117

Available from: 2014-11-17 Created: 2014-11-05 Last updated: 2014-11-17Bibliographically approved

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Caputo, Barbara
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Computer Vision and Active Perception, CVAP
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