Determination of human skin optical properties from hyper spectral data with deep-learning neural networks (Conference Presentation)

In the current report, we present further developments of a unified Monte Carlo-based computational framework and explore the potential of the emerging deep-learning neural networks for the determination of human skin optical properties. The hyperspectral data is acquired at each pixel as a function of time, by varying the illumination/detection wavelength and polarization of light. Subsequently, the signature of the detected signal within the tissues is estimated by a deep learning algorithm with supervised training based on a Monte Carlo modelling and then fit for the scattering and absorption properties of the tissue. The algorithm provides an estimation of parameters such as distributions of melanin, blood vessels, oxygenation, assessment of hyper vascularization and metabolism which are particularly critical for assessment of darkly and lightly pigmented skin lesions including moles, freckles, vitiligo, etc. The results of simulations are compared with exact analytical solutions, phantom studies and traditional diffuse reflectance spectroscopic point measurements. The computational solution is accelerated by the graphics processing units (GPUs) in a cloud-computing environment providing near-instant access to the results of analysis.