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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| Format: | Article |
| Language: | en |
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2023
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| 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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| _version_ | 1833419291922268160 |
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| author | Hui Ling Chua, Hui Ling Chua Audrey Huong, Audrey Huong Xavier Ngu, Xavier Ngu |
| author_facet | Hui Ling Chua, Hui Ling Chua Audrey Huong, Audrey Huong Xavier Ngu, Xavier Ngu |
| author_sort | Hui Ling Chua, Hui Ling Chua |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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 |
| format | Article |
| id | my.uthm.eprints-10605 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| record_format | eprints |
| spelling | my.uthm.eprints-106052024-01-15T07:30:21Z http://eprints.uthm.edu.my/10605/ Characterization of ink-based phantoms with deep networks and photoacoustic method Hui Ling Chua, Hui Ling Chua Audrey Huong, Audrey Huong Xavier Ngu, Xavier Ngu T Technology (General) 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 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10605/1/J16588_074b5002f1811663781fab31808efd46.pdf Hui Ling Chua, Hui Ling Chua and Audrey Huong, Audrey Huong and Xavier Ngu, Xavier Ngu (2023) Characterization of ink-based phantoms with deep networks and photoacoustic method. Journal of Autonomous Intelligence (, 6 (3). pp. 1-15. https://doi.org/10.32629/jai.v6i3.621 |
| spellingShingle | T Technology (General) Hui Ling Chua, Hui Ling Chua Audrey Huong, Audrey Huong Xavier Ngu, Xavier Ngu Characterization of ink-based phantoms with deep networks and photoacoustic method |
| title | Characterization of ink-based phantoms with deep networks and photoacoustic method |
| title_full | Characterization of ink-based phantoms with deep networks and photoacoustic method |
| title_fullStr | Characterization of ink-based phantoms with deep networks and photoacoustic method |
| title_full_unstemmed | Characterization of ink-based phantoms with deep networks and photoacoustic method |
| title_short | Characterization of ink-based phantoms with deep networks and photoacoustic method |
| title_sort | characterization of ink-based phantoms with deep networks and photoacoustic method |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10605/1/J16588_074b5002f1811663781fab31808efd46.pdf http://eprints.uthm.edu.my/10605/ https://doi.org/10.32629/jai.v6i3.621 |
| url_provider | http://eprints.uthm.edu.my/ |