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Advisor(s)
Abstract(s)
The estimation of the spectral absorption coefficient of biological
tissues provides valuable information that can be used in diagnostic procedures.
Such estimation can be made using direct calculations from invasive spectral
measurements or though machine learning algorithms based on noninvasive or
minimally invasive spectral measurements. Since in a noninvasive approach, the
number of measurements is limited, an exploratory study to investigate the use of
artificial generated data in machine learning techniques was performed to
evaluate the spectral absorption coefficient of the brain cortex. Considering the
spectral absorption coefficient that was calculated directly from invasive
measurements as reference, the similar spectra that were estimated through
different machine learning approaches were able to provide comparable
information in terms of pigment, DNA and blood contents in the cortex. The best
estimated results were obtained based only on the experimental measurements,
but it was also observed that artificially generated spectra can be used in the
estimations to increase accuracy, provided that a significant number of
experimental spectra are available both to generate the complementary artificial
spectra and to estimate the resulting absorption spectrum of the tissue.
Description
Keywords
Tissue spectroscopy Diffuse reflectance Absorption coefficient Brain cortex DNA content Blood content Pigment detection Machine learning Generative models
Citation
Publisher
Samara National Research University, Russian Federation