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The number of melanoma cancer-related death has increased over the last few years due to the new solar habits. Early diagnosis has become the best prevention method. This work presents a melanoma diagnosis architecture based on the collaboration of several multi-label case-based reasoning subsystems called DERMA. The system has to face several challenges that include data characterization, pattern matching, reliable diagnosis and self-explanation capabilities. Experiments using two subsystems specialized in confocal and dermoscopy data from images respectively have provided promising results to help experts assess melanoma patterns.
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