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

doi:10.1002/jbio.202300035

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DOI Доступ к ресурсу на сайте издателя			10.1002/jbio.202300035

Title: Implementation of data fusion to increase the efficiency of classification of precancerous skin states using in vivo bimodal spectroscopic technique
Creator: Kupriyanov, Valentin
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Blondel, Walter
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Daul, Christian
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Amouroux, Marine
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Kistenev, Yury V.
Date: 2023
DOI: 10.1002/jbio.202300035
Description: 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%.

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collection(s): Физический факультет
Identifier: смотреть в электронном каталоге НБ ТГУ
Type: статьи в журналах
Source: Journal of biophotonics. 2023. Vol. 16, № 7. P. e202300035 (1-14)
Language: eng

Created: 11-01-2024

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