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. 2021 May 7;15:634063. doi: 10.3389/fnins.2021.634063

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Copyright © 2021 Oladosu, Liu, Pike, Koch, Metz and Zhang.

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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Feature ranks and classification accuracies in lesion core-shell analysis. Top Panel: The mean (standard deviation) rankings of the nine diffusion features used in this analysis based on the linear recursive feature elimination (RFE) algorithm in SVM. Bottom Panel: Accuracy of the classification models (SVM-IC) achieved by adding features one at a time, starting from the highest to the lowest rankings. The stars indicate features that contribute to significant improvement in classification accuracy (^****p < 0.0001).