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. 2022 May 20;12:8508. doi: 10.1038/s41598-022-12486-w

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© The Author(s) 2022

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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Boxplot showing the effect of bottleneck narrowing with attention on the segmentation of FAZ area. Narrowing of channel with attention at the bottleneck of LWBNA_Unet is significantly effective for precise segmentation of FAZ, irrespective different model training strategies.