The nature of statistical learning theory
The nature of statistical learning theory
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Melanoma Prediction Using Data Mining System LERS
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern analysis of dermoscopic images based on Markov random fields
Pattern Recognition
Scale invariant descriptors in pattern analysis of melanocytic lesions
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Malignant melanoma is the most deadly form of skin lesion. Early diagnosis is of critical importance to patient survival. Existent visual recognition algorithms for skin lesions classification focus mostly on segmentation and feature extraction. In this paper instead we put the emphasis on the learning process by using two kernel-based classifiers. We chose a discriminative approach using support vector machines, and a probabilistic approach using spin glass-Markov random fields. We benchmarked these algorithms against the (to our knowledge) state-of-the-art method on melanoma recognition, exploring how performance changes by using color or textural features, and how it is affected by the quality of the segmentation mask. We show with extensive experiments that the support vector machine approach outperforms the existing method and, on two classes out of three, it achieves performances comparable to those obtained by expert clinicians.