Premium
Learning methods for melanoma recognition
Author(s) -
Torre Elisabetta La,
Caputo Barbara,
Tommasi Tatiana
Publication year - 2010
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20261
Subject(s) - computer science , artificial intelligence , support vector machine , segmentation , pattern recognition (psychology) , machine learning , k nearest neighbors algorithm , artificial neural network , focus (optics) , feature extraction , process (computing) , skin cancer , melanoma diagnosis , feature (linguistics) , melanoma , cancer , medicine , linguistics , philosophy , physics , cancer research , optics , operating system
Abstract 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. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 316–322, 2010