Applying Distance Metric Learning in a Collaborative Melanoma Diagnosis System with Case-Based Reasoning

Abstract

Current social habits in solar exposure have increased the appearance of melanoma cancer in the last few years. The highest mortality rates in dermatological cancers are caused for this illness. In spite of it, recent studies demonstrate that early diagnosis increases life expectancy. This work introduces a way to classify dermatological cancer with highest rates of accuracy, specificity and sensitivity. The approach is the result of the improvement of previous works that combine information of two of the most important non-invasive image techniques: Reflectance Confocal Microscopy and Dermatoscopy. Current work achieve better results than the previous systems by the use of Distance Metric Learning to the different Case Memories.