Characterization of ink-based phantoms with deep networks and photoacoustic method

This study aims to explore the feasibility of using an in-house developed photoacoustic (PA) system for predicting blood phantom concentrations using a pretrained Alexnet and a Long Short-Term Memory (LSTM) network. In two separate experiments, we investigate the performance of our strategy using a point laser source and a color-tunable Light-Emitting Diode (LED) as the illumination source. A single-point transducer is employed to measure signal change by adding ten different black ink concentrations into a tube. These PA signals are used for training and testing the employed deep networks. We found that the LED system with light wavelength of 450 nm gives the best characterization performance. The classification accuracy of the Alexnet and LSTM models tested on this dataset shows an average value of 94% and 96%, respectively, making this a preferred light wavelength for future operation. Our system may be used for the noninvasive assessment of microcirculatory changes in humans.