Machine learning methods for classifying Raman skin spectra.

Abstract

In this paper, the possibility of using machine learning methods to classify Raman skin spectra is considered.
Registration of Raman skin spectra was carried out using a portable spectroscopic setup. More than 500 patients participated in the study. For each patient, the spectrum was recorded from the healthy skin area and from the area with the disease. In total, 1225 spectra were used in the work: 609 spectra of healthy skin and 616 spectra with various skin diseases (benign and malignant neoplasms). In addition to the Raman scattering spectra, we obtained information about the actual diagnoses for each patient. These diagnoses were made by doctors of the Samara Regional Clinical Oncology Dispensary Samara the basis of histological analysis.
Classification models were developed for three cases: healthy skin versus skin with diseases, benign neoplasms versus malignant and melanoma versus pigmented nevus.
To reduce the dimensionality of the data, the method of multivariate curve resolution alternating least squares (MCR-ALS) was used. Thirty skin components and their relative concentrations in the test sample were obtained, these data were subsequently used as classification parameters.
For classification purposes, the following machine learning methods were used: logistic regression, random forest, k-least squares method and gradient boosting. The effectiveness of the developed classifiers was compared. The classification accuracy varies from 73% to 90%.


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Ksenia Tomnikova
Samara National Reserch University
Russia

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