Electrical impedance tomography (EIT) is a non-destructive, non-invasive, and non-radioactive imaging technique used for reconstructing the internal conductivity distribution of a sensing domain. Performing EIT often requires large, stationary benchtop equipment that can be expensive and impractical. Other researchers have attempted to make portable EIT systems, but they all rely on external computation for image reconstruction/data analysis. This study outlines the development of a low-cost, portable, and wireless EIT data acquisition (DAQ) system that is capable of independently performing image reconstructions on-board. With the proposed system, EIT can be performed on carbon fiber reinforced polymers to spatially locate damages. Since EIT reconstruction algorithms can be extremely computationally intensive, this study has also developed an alternative deep-learning algorithm that leverages the compressed-sensing technique to strategically train a neural network. The proposed neural network has not only achieved comparable results to traditional iterative algorithms, but it can do so in a fraction of the time.