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. 2020 Jun 25;10:10333. doi: 10.1038/s41598-020-67178-0

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© The Author(s) 2020

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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Flowchart of this study. Briefly, Image Set A (image patches which were annotated as 9-categry in tissue slides from colorectal cancer, downloaded from the published database) was used as training set to train multiple neural networks (CNNs). The InceptionResNet V2 was locked-down after category-recognition training, due to highest accuracy in to recognizing the image patches from Image Set B and calculating the proportions of each tissue category in each whole slide (pie charts), after discarding Background. Image Set B was separated into training set (60%) and test set (40%), and the training set with the proportions of 8-tissue category was sent into multiple machine classifiers to construct the predictive model. The test set was applied to test the accuracy of each machine predictive model. Validated the performance of each predictive model by using Image Set C. Finally, Gradient Boosting Decision Tree was chosen as our predictive model.