In this work, deep learning neural network architecture was applied to the well-known diffuse optic tomography problem. Frequency Domain Diffuse Optic Tomography (FDDOT) modality was selected as reference simulation system. Sources and detectors were placed on tissue imaging geometry. Back-reflection geometry has 8 sources and 8 detectors on tissue imaging geometry. Background phantom tissue absorption coefficient ma=0.3 cm-1, scattering coefficient ms=100 cm-1. One inclusion which its absorption coefficient ma=0.7 cm-1 was embedded in two different location in 20×20×20 grid sized tomographic cube. These two different scenarios were tested to reconstruct the inclusion image by using Matlab™ deep learning functions. Two different inclusion images were successfully reconstructed with the similar shape of original inclusion in the same 3d location. In deep learning structure, Matlab’s convolution layer, cross channel normalization layer, fully connected layer, regression layer was used. In training network options, stochastic gradient descent with momentum method with validation data was selected. In this work, instead of using classical inverse problem solution algorithms deep learning method was used.