Explainable Analysis of Deep Learning Models for Coronavirus Disease (COVID-19) Classification with Chest X-Ray Images: Towards Practical Applications

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ISSN Print: 2164-2788
ISSN Online: 2164-2796
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"Explainable Analysis of Deep Learning Models for Coronavirus Disease (COVID-19) Classification with Chest X-Ray Images: Towards Practical Applications"
written by Eri Matsuyama, Haruyuki Watanabe, Noriyuki Takahashi,
published by Open Journal of Medical Imaging, Vol.12 No.3, 2022

has been cited by the following article(s):

[1]	Early Diagnosis of COVID-19 Images Using Optimal CNN Hyperparameters
	Shazly… - Diagnostics, 2023

[2]	Ensemble of Deep Neural Networks based on Condorcet's Jury Theorem for screening Covid-19 and Pneumonia from radiograph images
	Computers in Biology and Medicine, 2022

[1]	Explainable Artificial Intelligence in Medical Image Analysis: State of the Art and Prospects 2023 XXVI International Conference on Soft Computing and Measurements (SCM), 2023 DOI:10.1109/SCM58628.2023.10159033

[2]	Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability Scientific Reports, 2023 DOI:10.1038/s41598-023-40472-3

[3]	Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability Scientific Reports, 2023 DOI:10.1038/s41598-023-40472-3

[4]	Ensemble of Deep Neural Networks based on Condorcet’s Jury Theorem for screening Covid-19 and Pneumonia from radiograph images Computers in Biology and Medicine, 2022 DOI:10.1016/j.compbiomed.2022.105979

[5]	Early Diagnosis of COVID-19 Images Using Optimal CNN Hyperparameters Diagnostics, 2022 DOI:10.3390/diagnostics13010076

[6]	Ensemble of Deep Neural Networks based on Condorcet’s Jury Theorem for screening Covid-19 and Pneumonia from radiograph images Computers in Biology and Medicine, 2022 DOI:10.1016/j.compbiomed.2022.105979

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