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Learning Methods for Melanoma Recognition
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DOI：10.1002/ima.20261.png)
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Abstract
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Melanoma is the most deadly skin cancer. Early diagnosis is a challenge for clinicians. Current algorithms for skin lesions' classification focus mostly on segmentation and feature extraction. This article instead puts the emphasis on the learning process, testing the recognition performance of three different classifiers: support vector machine (SVM), artificial neural network and k-nearest neighbor. Extensive experiments were run on a database of more than 5000 dermoscopy images. The obtained results show that the SVM approach outperforms the other methods reaching an average recognition rate of 82.5% comparable with those obtained by skilled clinicians. If confirmed, our data suggest that this method may improve classification results of a computer-assisted diagnosis of melanoma. (C) 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 316-322, 2010; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ima.20261
Keywords:
melanoma recognition
computer assisted diagnosis
dermoscopy
support vector machines
kernel methods
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