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. 2018 Oct 30;8:16016. doi: 10.1038/s41598-018-34300-2

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© The Author(s) 2018

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 area under receiver operating characteristic (AUROC) values for various classification models from AlexNet-Convolutional Neuron Networks (CNN), conventional Artificial Neural Networks (ANN), non-linear Multinomial Logistic Regression (MLR), linear Support Vector Machines (SVM) and feature-ranking based Random Forests (RF) algorithms. AUROC was evaluated for CNN and ANN, MLR, SVM and RF with (A) morphological features (Features 1–21), (B) textural features (Features 21–130) and (C) both morphological and textural features (Feature 1–130). N.S.: non-significant difference, *adjusted p value is less than 0.005.