title: Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy.
abstract: 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.
authors.affiliation: Centre de Recherche en Automatique de Nancy, UMR 7039, Nancy.
authors.forename: F
authors.initials: F
authors.lastname: Abdat
authors.forename: M
authors.initials: M
authors.lastname: Amouroux
authors.forename: Y
authors.initials: Y
authors.lastname: Guermeur
authors.forename: W
authors.initials: W
authors.lastname: Blondel
mesh_terms: D000818:Animals; D051379:Mice; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated / Q000379:methods*; D011230:Precancerous Conditions / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*; D060388:Support Vector Machine