Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/77615
Title: Statistics-based Classification Approach for Hyperspectral Dermatologic Data Processing
Authors: Martínez Vega, Beatriz 
Quevedo Gutiérrez, Eduardo Gregorio 
León Martín, Sonia Raquel 
Fabelo Gómez, Himar Antonio 
Ortega Sarmiento, Samuel 
Marrero Callicó, Gustavo Iván 
Castano, Irene
Carretero, Gregorio
Almeida, Pablo
Garcia, Aday
Hernandez, Javier A.
Uteng, Stig
Godtliebsen, Fred
UNESCO Clasification: 320106 Dermatología
320713 Oncología
Keywords: Data Classification
Hyperspectral Imaging
Skin Cancer
Statistical Analysis
Issue Date: 2020
Conference: 35th Conference on Design of Circuits and Integrated Systems - DCIS 2020
Abstract: Hyperspectral Imaging (HSI) for dermatology applications lacks a physical model to differentiate between cancerous or non-cancerous pigmented skin lesions. In this paper the statistical properties of a set of HSI data are exploited as an alternative to this limitation. The hyperspectral dermatologic database employed in the experiments is composed by 40 noncancerous and 36 cancerous pigmented skin lesions (PSLs) obtained from 61 patients. The preliminary experiments suggest the potential of a simple statistics metrics, such as the coefficient of variation, to distinguish between cancerous and non-cancerous PSLs using hyperspectral data. A sensitivity result of 100% was achieved in the test set providing an overall accuracy classification of 80%.
URI: http://hdl.handle.net/10553/77615
ISBN: 9781728191324
DOI: 10.1109/DCIS51330.2020.9268646
Source: 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS)
Appears in Collections:Actas de congresos
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