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Monte Carlo Simulations (MCSs) allow for the estimation of photon propagation through media given knowledge of the geometry and optical properties. Previous research has demonstrated that the inverse of this problem may be solved as well, where neural networks trained on photon distributions can be used to estimate refractive index, scattering and absorption coefficients. To extend this work, time-dependent MCSs are used to generate data sets of photon propagation through various media. These simulations were treated as stacks of 2D images in time and used to train convolutional networks to estimate tissue parameters. To find potential features that drive network performance on this task, networks were randomly generated. Generated networks were then trained. The networks were validated using 4-fold cross validation. The consistently performing top 10 networks typically had an emphasis on convolutional chains and convolutional chains ending in max pooling.