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. 2018 Nov 15;12:804. doi: 10.3389/fnins.2018.00804

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Copyright © 2018 Yang, Yan, Zhang, Han, Nan, Hu, Hu, Yan, Zhang, Cheng, Ge, Cui, Zhao and Wang.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Study design. Patient underwent magnetic resonance imaging (MRI) before surgery. Slices with tumor were selected and segmented as rectangular region of interest (ROI) after images format conversion. Then, data were split into training, validation and test dataset. The data used in five-fold cross-validation were augmented 14 times, and keep the test dataset unchanged. Finally, AlexNet and GoogLeNet were explored and evaluated by training from scratch and fine-tuning from pre-trained model.