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. 2019 Dec 10;9:18738. doi: 10.1038/s41598-019-55373-7

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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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Architecture of eDenseYOLO with a backend network of DenseNet201. The output layers of eDenseYOLO, i.e., YOLO v2 with DenseNet201, were modified for improved robustness with respect to disease-pattern size. If the input is 256 × 256, the feature map for the last layer is 8 × 8, 16 × 16, or 32 × 32 with a skip connection.