Source: Engineering applications of artificial intelligence. Unidade: EP
Subjects: TOMOGRAFIA, IMPEDÂNCIA ELÉTRICA, ANATOMIA, REDES NEURAIS
ABNT
OKAMURA, Lucas Hideki Takeuchi et al. Thorax and internal organs boundary geometries determination using convolutional neural networks in electrical impedance tomography. Engineering applications of artificial intelligence, v. 136, p. 1-9, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.engappai.2024.108918. Acesso em: 09 nov. 2025.APA
Okamura, L. H. T., Costa, L. H. da, Duran, G. C., Sato, A. K., Ueda, E. K., Takimoto, R. Y., et al. (2024). Thorax and internal organs boundary geometries determination using convolutional neural networks in electrical impedance tomography. Engineering applications of artificial intelligence, 136, 1-9. doi:10.1016/j.engappai.2024.108918NLM
Okamura LHT, Costa LH da, Duran GC, Sato AK, Ueda EK, Takimoto RY, Martins T de C, Tsuzuki MSG. Thorax and internal organs boundary geometries determination using convolutional neural networks in electrical impedance tomography [Internet]. Engineering applications of artificial intelligence. 2024 ; 136 1-9.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.engappai.2024.108918Vancouver
Okamura LHT, Costa LH da, Duran GC, Sato AK, Ueda EK, Takimoto RY, Martins T de C, Tsuzuki MSG. Thorax and internal organs boundary geometries determination using convolutional neural networks in electrical impedance tomography [Internet]. Engineering applications of artificial intelligence. 2024 ; 136 1-9.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.engappai.2024.108918
