|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
Hernandez, Javier A.
|UNESCO Clasification:||320106 Dermatología
|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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