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. 2019 Jul 19;9:10509. doi: 10.1038/s41598-019-46718-3

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© The Author(s) 2019

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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Cancer Classification Pipeline. (1) Kidney Whole slide images. (2) 512*512 patches extracted from images with 50% overlap and background removed using pixel thresholding. (3) Patches from normal and cancerous slides fed to the deep network. (4) Patches classified as cancerous or non-cancerous. (5) High probability patches identified by the trained network and binary mask is applied. (6) The patches from three subtypes used to train a similar deep architecture for a three-way classification. (7) Features extracted from the penultimate layer of the network and fed to DAG-SVM and a three-way classification is performed by it.