Implementation of Data Fusion to increase the efficiency of classification of pre-cancerous skin states using in vivo bimodal spectroscopic technique.

This study presents the results of the classification of diffuse reflectance (DR) spectra and multi-excitation 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), multi-layered 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. The results of the study showed that in most of cases the use of data fusion methods increased the average multiclass classification accuracy from 2 up to 4%. The highest accuracy of multi-class classification was obtained using the manual optimization of weights and reached 94.41%.