Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy
Faiza Abdat, Marine Amouroux, Yann Guermeur and Walter Blondel
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.