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. 2019 Apr 5;9:5746. doi: 10.1038/s41598-019-42276-w

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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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The difference of receiver operating characteristic curve according to image sets in the convolutional neural network (CNN) classifier (metric 4). The CNN showed the best performance with apparent diffusion coefficient (ADC) images, compared with the performance achieved when a combination of contrast-enhanced T1-weighted (CE-T1WI) and ADC images were used or when CE-T1WI images alone were used.