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. 2020 Oct 21;10:17980. doi: 10.1038/s41598-020-74599-4

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Results for deep learning accuracy and loss over 200 epochs for both the training and validation datasets. The model accuracy in the training set reached 95% accuracy, with overall accuracy of 70%. The validation set reached 100% accuracy. Loss improved as expected over 200 epochs, with the lowest loss of the model’s performance on the test set of 0.56.