Implementation of data fusion to increase the efficiency of classification of precancerous skin states using in vivo bimodal spectroscopic technique
Abstract
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. 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 multiclass classification was obtained using the manual optimization of weights and reached 94.41%.