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. 2019 Apr 18;9:6268. doi: 10.1038/s41598-019-42557-4

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© The Author(s) 2019

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Schematic representation of the network architecture. conv = convolution, pool = pooling, GAP = Global Average Pooling, fc = fully connected. Convolutions and pooling sizes are reported as height × width/stride. The additional number indicates the number of feature maps for convolutions and the number of output neurons for the fully connected layer. Circled pluses indicate an addition operation.