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. 2020 Mar 27;10:5640. doi: 10.1038/s41598-020-62347-7

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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 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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Overall architecture of the Visual Geometry Group-16 (VGG 16) model. VGG-16 comprises five blocks and three fully connected layers. Each block comprises some convolutional layers, followed by a max-pooling layer. After the output matrix has been flattened following block 5, there are two fully connected layers for binary classification. The deep neural network used ImageNet parameters as the default weights of blocks 1–4.