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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10605/1/J16588_074b5002f1811663781fab31808efd46.pdf http://eprints.uthm.edu.my/10605/ https://doi.org/10.32629/jai.v6i3.621 |
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| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English |
| Summary: | 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 LightEmitting 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 |
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