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. 2021 Oct 19;11:20691. doi: 10.1038/s41598-021-98814-y

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© The Author(s) 2021

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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The prediction results of tumors/normal samples and confusion matrix for 11 cancers. The prediction accuracies of (A) normal tissues and tumors (left), as well as normal tissues and 11 cancer types (right). The x-axis is 100 training/testing times (epochs); and the y-axis presents accuracy of training/testing. (B) The confusion matrix is generated from 4908 independent images. The y-axis is true classes (ground truth) of validation samples and x-axis is prediction classes of CNN model. The diagonal cells are the sample counts of correct prediction/classification. (C) The hierarchical clustering of 1228 samples by using Pearson’s r of gene expression.