Bibliografía

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[4] M.V. Valueva, N.N. Nagornov, P.A. Lyakhov, G.V. Valuev, and N.I. Chervyakov. “Application of the residue number system to reduce hardware costs of the convolutional neural network implementation,” Mathematics and Computers in Simulation, 2020.

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[9] T. Rahman, et al., “Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images,” Computers in Biology and Medicine, 2021.

[10] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri and R. M. Summers, “ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,” Computing Research Repository, 2017.

[11] D. Kermany, K. Zhang, M. Goldbaum, “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,” Mendeley Data, V2, 2018.

[12] A. Krizhevsky, I. Sutskever, G.E. Hinton, “Imagenet classification with deep convolutional neural networks,” Curran Associates, 2012.

[13] J. Howard, S. Gugger. “Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD,” Canadá: O’Reilly Media, 2020.

[14] L. N. Smith, “No more pesky learning rate guessing games,” Computing Research Repository, 2015.


Cómo se cita

M. Gómez-Macedo, J. Olveres-Montiel, G. Fuentes-Pineda, B. Escalante-Ramírez y F. Arámbula-Cosío, "Detección de COVID-19 en radiografías de tórax mediante aprendizaje profundo," TIES, Revista de Tecnología e Innovación en Educación Superior, no. 7, abril, 2022. [En línea]. Disponible en: https://ties.unam.mx/ [Consultado en mes día, año].