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. 2019 Aug 29;9:12529. doi: 10.1038/s41598-019-48738-5

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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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T1-weighted post contrast MR scan. (a) GL261, mouse glioma, (b) U87, human glioma, (c) Daoy, human medulloblastoma. (d) Schematic of workflow for feature extraction. Images are imported into a custom program for visualization and segmentation of the tumor region. The selected region used to extract 1^st and 2^nd order image features. These features are then used to construct the three different supervised classifiers: Decision Tree, Random Forest, and Support Vector Machine.