A machine-learning model for quantitative characterization of human skin using photothermal radiometry and diffuse reflectance spectroscopy
Construction and evaluation of predictive models (Random Forest) for 14 free parameters of a numerical model of light-tissue interaction applied to a four-layer optical model of human skin as surrogates for estimation using a computationally expensive inverse Monte-Carlo approach.
Verdel, N., Tanevski, J., Džeroski, S., & Majaron, B. (2019). A machine-learning model for quantitative characterization of human skin using photothermal radiometry and diffuse reflectance spectroscopy. In Photonics in Dermatology and Plastic Surgery 2019 (Vol. 10851, p. 1085107). International Society for Optics and Photonics.
Verdel, N., Tanevski, J., Džeroski, S., & Majaron, B. (2019). Hybrid technique for characterization of human skin using a combined machine learning and inverse Monte Carlo approach. In Novel Biophotonics Techniques and Applications V (Vol. 11075, p. 110751K). International Society for Optics and Photonics.
Verdel, N., Tanevski, J., Džeroski, S., & Majaron, B. (2020). Predictive model for quantitative analysis of human skin using photothermal radiometry and diffuse reflectance spectroscopy. Biomedical Optics Express 11(3), 1679-1696.