Open AccessOn the spectral signature of melanoma: a non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions
Author(s) -
Arturo Pardo,
José A. Gutiérrez-Gutiérrez,
Ilze Lihacova,
José Miguel López Higuera,
Olga M. Conde
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.006283
Subject(s) - hyperspectral imaging , thresholding , false positive paradox , melanoma , spectral signature , artificial intelligence , cancer detection , pattern recognition (psychology) , signature (topology) , skin cancer , spectral imaging , false positives and false negatives , parametric statistics , computer science , medicine , cancer , image (mathematics) , mathematics , statistics , remote sensing , geometry , cancer research , geology , physics , quantum mechanics
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.
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