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. 2021 Mar 16;11:6006. doi: 10.1038/s41598-021-85570-2

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© The Author(s) 2021

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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GradCAM-based heatmaps for the developed model. Each sub-figure shows input image and its label on the left side and GradCAM and prediction probability on the right side. (a) and (b) show PA and LAT views of a true positive case from test set #1 where the fracture is easily visible. (c) and (d) show both views of a true positive from test set #2 where the fracture is hardly visible and (e) and (f) show both views of a false negative case from test set #2 where the pipeline predicts them as normal.