Citations refer to the original publication, not to a Scieee localized version.
Agarwal, D., Berbís, M. Á., Martín Noguerol, T., Luna, A., García Parrado, S. C., Torre Díez, I. D. L., & Valladolid, U. D. (2022). End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis. Mathematics, 10(15), 2575. https://doi.org/10.3390/math10152575
Agarwal, Deevyankar, et al. “End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis.” Mathematics, vol. 10, no. 15, 2022, pp. 2575. https://doi.org/10.3390/math10152575.
Agarwal, Deevyankar, Manuel Álvaro Berbís, Teodoro Martín Noguerol, Antonio Luna, Sara Carmen García Parrado, Isabel de la Torre Díez, and Universidad de Valladolid. “End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis.” Mathematics 10, no. 15 (2022): 2575. https://doi.org/10.3390/math10152575.
Agarwal, D. et al. (2022) ‘End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis’, Mathematics, 10(15), pp. 2575. Available at: https://doi.org/10.3390/math10152575.
D. Agarwal et al., “End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis,” Mathematics, vol. 10, no. 15, pp. 2575, 2022, doi: 10.3390/math10152575.
@article{agarwal2022endtoend,
author = {Agarwal, Deevyankar and Berbís, Manuel Álvaro and Martín Noguerol, Teodoro and Luna, Antonio and García Parrado, Sara Carmen and Torre Díez, Isabel de la and Valladolid, Universidad de},
title = {End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis},
journal = {Mathematics},
year = {2022},
volume = {10},
number = {15},
pages = {2575},
publisher = {MDPI},
doi = {10.3390/math10152575},
url = {https://doi.org/10.3390/math10152575}
}
