A dataset of optical spectra and clinical features acquired on human healthy skin and on skin carcinomas.

Optical spectroscopy is studied to contribute to skin cancer diagnosis. Indeed, optical spectra are modified along cancer progression and provide complementary information (e.g.

Implementation of data fusion to increase the efficiency of classification of precancerous skin states using in vivo bimodal spectroscopic technique.

This study presents the results of the classification of diffuse reflectance (DR) spectra and multiexcitation autofluorescence (AF) spectra that were collected in vivo from precancerous and benign skin lesions at three different source detector separation (SDS) values. Spectra processing pipeline consisted of dimensionality reduction, which was performed using principal component analysis (PCA), followed by classification step using such methods as support vector machine (SVM), multilayered perceptron (MLP), linear discriminant analysis (LDA), and random forest (RF). In order to increase the efficiency of lesion classification, several data fusion methods were applied to the classification results: majority voting, stacking, and manual optimization of weights.