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. 2021 Dec 21;11:24375. doi: 10.1038/s41598-021-03813-8

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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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An automated image processing framework quantifies features of cellular cytoskeletal and morphological structure from single cell images. These features were used to train parameters of a classification model and its performance was evaluated using validation data. The algorithm was able to accurately discriminate cancer cells from non-cancer cells and identify individual features that had the greatest influence on classification outcome.