On the spectral signature of melanoma: a non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions

Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.MINECO (Ministerio de Economía y Competitividad), Instituto de Salud Carlos III (ISCIII) (DTS15/00238, DTS17/00055, TEC2016-76021-C2-2-R); CIBER-BBN; IDIVAL (INNVAL 16/02); MECD (Ministerio de Educación, Cultura y Deporte) (FPU16/05705)