DCT-SVM based multi-classification of mouse skin precancerous stages from autofluorescence and diffuse reflectance data
This paper deals with multi-classification of skin precancerous stages based on bimodal AutoFluorescence (AF) spectroscopy and Diffuse Reflectance (DR) spectroscopy. The proposed data processing method is based on Discrete Cosine Transform (DCT) to extract discriminant spectral features and on Support Vector Machine to classify. Results show that DCT gives better results for AF spectra than for DR spectra. This study shows that bimodality and spectral resolution allow an increase in diagnostic accuracy. This accuracy can get as high as 79% when combining the 3 distances for bimodality.