Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks.

Journal: Journal of biomedical optics

PMID: 39822706

Abstract

SIGNIFICANCE: Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD).

Authors

Teo Manojlović

University of Rijeka, Faculty of Engineering, Rijeka, Croatia.
Tadej Tomanič

University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia.
Ivan Štajduhar

Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka, Croatia; Faculty of Engineering and Natural Sciences, Sabanci University, Üniversite Cd. No:27, Tuzla, Istanbul, Turkey. Electronic address: istajduh@riteh.hr.
Matija Milanič

University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia.

Keywords

Algorithms Animals Bayes Theorem Computer Simulation Humans Hyperspectral Imaging Image Processing, Computer-Assisted Neural Networks, Computer Skin Skin Physiological Phenomena

External Resources

View on PubMed Access via DOI PubMed (39822706)

Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks.

Abstract

Authors

Keywords

External Resources

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