Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy | Zendy

F. Abdat | Zendy; M. Amouroux | Zendy; Yann Guermeur | Zendy; Walter Blondel | Zendy

Open Access

Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy

Author(s) -

F. Abdat,

M. Amouroux,

Yann Guermeur,

Walter Blondel

Publication year - 2011

Publication title -

optics express

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.394

H-Index - 271

ISSN - 1094-4087

DOI - 10.1364/oe.20.000228

Subject(s) - discrete cosine transform , support vector machine , artificial intelligence , pattern recognition (psychology) , feature selection , bimodality , computer science , feature (linguistics) , optics , materials science , physics , image (mathematics) , linguistics , philosophy , quantum mechanics , galaxy

This paper deals with multi-class classification of skin pre-cancerous stages based on bimodal spectroscopic features combining spatially resolved AutoFluorescence (AF) and Diffuse Reflectance (DR) measurements. A new hybrid method to extract and select features is presented. It is based on Discrete Cosine Transform (DCT) applied to AF spectra and on Mutual Information (MI) applied to DR spectra. The classification is performed by means of a multi-class SVM: the M-SVM2. Its performance is compared with the one of the One-Versus-All (OVA) decomposition method involving bi-class SVMs as base classifiers. The results of this study show that bimodality and the choice of an adequate spatial resolution allow for a significant increase in diagnostic accuracy. This accuracy can get as high as 81.7% when combining different distances in the case of bimodality.

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