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. 2019 Nov 14;9:16770. doi: 10.1038/s41598-019-53217-y

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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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Examples of images from videos of semen samples with different concentrations (columns) and the four image representations used to train the neural network-based algorithms (rows). Sperm concentration; (A) 4 per x106/mL, (B) 33 per x106/mL, (C) 105 per x106/mL, (D) 192 per x106/mL, and (E) 350 per x106/mL. Image representation; (1) original video, (2) sparse optical flow, (3) dense optical flow, and (4) vertical frame matrix.