The Trade-off in Machine Learning Application for Electrical Impedance Tomography

Updata:01-01-1970

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By:Marlin Ramadhan Baidillah, Pratondo Busono

DOI: https://doi.org/10.30564/ese.v4i2.5000

Abstract: References:

[1] Brown, B.H., 2003. Electrical impedance tomography (EIT): A review. Journal of Medical Engineering & Technology. 27(3), 97-108. DOI: https://doi.org/10.1080/0309190021000059687 [2] Khambampati, A.K., Rahman, S.A., Sharma, S.K., et al., 2022. Nonlinear difference imaging to image local conductivity of single-layer graphene using electrical impedance tomography. IEEE Transactions on Instrumentation and Measurement. DOI: https://doi.org/10.1109/TIM.2022.3147894 [3] Yao, J., Takei, M., 2017. Application of Process Tomography to Multiphase Flow Measurement in Industrial and Biomedical Fields - A Review. IEEE Sensors Journal. pp. 1. DOI: https://doi.org/10.1109/JSEN.2017.2682929 [4] Hahn, G., Just, A., Dittmar, J., et al., 2008. Systematic errors of EIT systems determined by easily-scalable resistive phantoms. Physiological Measurement. 29(6), S163-172. DOI: https://doi.org/10.1088/0967-3334/29/6/S14 [5] Cook, R.D., Saulnier, G.J., Gisser, D.G., et al., 1994. ACT3: a high-speed, high-precision electrical impedance tomograph. IEEE Transactions on Biomedical Engineering. 41(8), 713-722. DOI: https://doi.org/10.1109/10.310086 [6] Baidillah, M.R., Iman, A.A.S., Sun, Y., et al., 2017. Electrical Impedance Spectro-Tomography based on Dielectric Relaxation Model. IEEE Sensors Journal. 17(24), 8251-8262. DOI: https://doi.org/10.1109/JSEN.2017.2710146 [7] Hoyle, B.S., Nahvi, M., 2008. Spectro-tomography - an electrical sensing method for integrated estimation of component identification and distribution mapping in industrial processes. 2008 IEEE Sensors. pp. 807-810. DOI: https://doi.org/10.1109/ICSENS.2008.4716564 [8] Ogawa, R., Baidillah, M.R., Akita, S., et al., 2020. Investigation of physiological swelling on conductivity distribution in lower leg subcutaneous tissue by electrical impedance tomography. Journal of Electrical Bioimpedance. 11(1), 19-25. DOI: https://doi.org/10.2478/joeb-2020-0004 [9] Marashdeh, Q., Warsito, W., Fan, L.S., et al., 2006. An Impedance Tomography system based on ECT sensor. IEEE Sensors Journal. 1(11), 1-7. [10] Wang, Q., Wang, M., Wei, K., et al., 2017. Visualization of Gas-Oil-Water Flow in Horizontal Pipeline Using Dual-Modality Electrical Tomographic Systems. IEEE Sensors Journal. 17(24), 8146-8156. DOI: https://doi.org/10.1109/JSEN.2017.2714686 [11] Gürsoy, D., Mamatjan, Y., Adler, A., et al., 2011. Enhancing impedance imaging through multimodal tomography. IEEE Transactions on Biomedical Engineering. 58(11), 3215-3224. DOI: https://doi.org/10.1109/TBME.2011.2165714 [12] Choridah, L., Kurniadi, D., Ain, K., et al., 2021. Comparison of Electrical Impedance Tomography and Ultrasonography for Determination of Solid and Cystic Lesion Resembling Breast Tumor Embedded in Chicken Phantom. Journal of Electrical Bioimpedance. 12(1), 63. DOI: https://doi.org/10.2478/joeb-2021-0008 [13] Dy, J.G., Brodley, C.E., Kak, A., et al., 2003. Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 25(3), 373-378. DOI: https://doi.org/10.1109/TPAMI.2003.1182100 [14] Rymarczyk, T., Kłosowski, G., Hoła, A., et al., 2022. Optimising the use of Machine learning algorithms in electrical tomography of building Walls: Pixel oriented ensemble approach. Measurement. 188, 110581. DOI: https://doi.org/10.1016/j.measurement.2021.110581 [15] Coxson, A., Mihov, I., Wang, Z., et al., 2022. Machine learning enhanced electrical impedance tomography for 2D materials. Inverse Problems. 38(8), 085007. [16] Tanaka, K., Prayitno, Y.A.K., Sejati, P.A., et al., 2022. Void fraction estimation in vertical gas-liquid flow by plural long short-term memory with sparse model implemented in multiple current-voltage system. Multiphase Science and Technology. 34(2). [17] Wu, Y., Jiang, D., Liu, X., et al., 2018. A Human-Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control. IEEE Transactions on Biomedical Circuits and Systems. 12(6), 1322-1333. DOI: https://doi.org/10.1109/TBCAS.2018.2878395 [18] Valueva, M.V., Nagornov, N.N., Lyakhov, P.A., et al., 2020. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation. 177, 232-243. DOI: https://doi.org/10.1016/j.matcom.2020.04.031 [19] Hochreiter, S., Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation. 9(8), 1735-1780. [20] Lee, H., Huang, C., Yune, S., et al., 2019. Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction. Scientific Reports. 9(1), 1-9. DOI: https://doi.org/10.1038/s41598-019-51779-5

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