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Advisor(s)
Abstract(s)
In this study, we used machine learning techniques to reconstruct the wavelength dependence of the absorption coefficient of human
normal and pathological colorectal mucosa tissues. Using only diffuse reflectance spectra from the ex vivo mucosa tissues as input to
algorithms, several approaches were tried before obtaining good matching between the generated absorption coefficients and the ones
previously calculated for the mucosa tissues from invasive experimental spectral measurements. Considering the optimized match for the
results generated with the multilayer perceptron regression method, we were able to identify differentiated accumulation of lipofuscin in
the absorption coefficient spectra of both mucosa tissues as we have done before with the corresponding results calculated directly from
invasive measurements. Considering the random forest regressor algorithm, the estimated absorption coefficient spectra almost matched
the ones previously calculated. By subtracting the absorption of lipofuscin from these spectra, we obtained similar hemoglobin ratios at
410/550 nm: 18.9-fold/9.3-fold for the healthy mucosa and 46.6-fold/24.2-fold for the pathological mucosa, while from direct calculations,
those ratios were 19.7-fold/10.1-fold for the healthy mucosa and 33.1-fold/17.3-fold for the pathological mucosa. The higher values obtained
in this study indicate a higher blood content in the pathological samples used to measure the diffuse reflectance spectra. In light of such
accuracy and sensibility to the presence of hidden absorbers, with a different accumulation between healthy and pathological tissues, good
perspectives become available to develop minimally invasive spectroscopy methods for in vivo early detection and monitoring of colorectal
cancer.The application of machine learning methods to noninvasivelike diffuse reflectance spectra allowed us to reconstruct the
absorption coefficient spectra of human healthy and pathological
mucosa tissues from the colorectal wall. Consequently, we were
able to obtain differentiated blood and pigment content in both
tissues, which can be used for the development of new noninvasive
diagnostic methods for colorectal cancer.
Description
Keywords
Diseases and conditions Blood Artificial neural networks Optical properties Machine learning Pathology Optical absorption Spectroscopy
Citation
Fernandes, L., Carvalho, S., Carneiro, I., Henrique, R., Tuchin, V.V., Oliveira, H.P., Oliveira, L.M. Diffuse reflectance and machine learning techniques to differentiate colorectal cancer ex vivo. Chaos, vol. 31, 053118.
Publisher
AIP Publishing