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. 2023 May 22;13:8213. doi: 10.1038/s41598-023-34943-w

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© The Author(s) 2023

Open AccessThis 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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A visual overview of ACCT components and process. (A) Weka and a set of training images are employed to create multiple classifiers through iterative training. (B) These classifiers are then evaluated in bulk against validation images and the best classifier is chosen by the user. (C) The chosen classifier is applied to the experimental dataset for cell quantification, producing a set of counted images and the number of cells in each image, as well as information about the cell morphology. This information includes the area, position, minimum and maximum intensity, circularity, skew, and more details about each cell, available on GitHub in the data availability section.