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. 2022 Feb 24;12:3155. doi: 10.1038/s41598-022-07092-9

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© The Author(s) 2022

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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(a) Learning curves for the training and validation data sets of the dNN. (b) The testing data was degraded with additive Gaussian noise with a standard deviation varying from 0 to 30% of the maximum pixel intensity. ROC curves were recorded, and the AUC was calculated. (c) The same method as panel b, mean and standard deviation of accuracy was recorded for each class. (d) The same noise addition method as panel b, mean and standard deviation of Matthews correlation coefficient was recorded with additive noise.