Our work applies neutral networks to solving forward and inverse problems in diffuse reflectance spectroscopy. Firstly, a neural network forward model is trained with Monte Carlo data so as to predict diffuse reflectance from given optical parameters. Secondly, an inverse model based on the neural network forward model is built to solve for optical parameters from diffuse reflectance, modified from the traditional Monte Carlo-based inverse model. Validation of our inverse model on experimentally measured phantom data is investigated.
We propose to use neural networks to learn and replace Monte Carlo (MC) simulations. Our neural networks are not only convenient to use but also are shown to be extremely time-saving compared to MC simulations with comparable accuracy. Furthermore, we employ the machine learning method called transfer learning to perform calibration between MC simulated diffuse reflectance and that measured by Diffuse Reflectance Spectroscopy (DRS). The transfer learning model is able to predict DRS measured diffuse reflectance spectrum by training the model using a small amount of DRS data.
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