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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 spread in D for the testing dataset (segmentation of FAZ area) obtained from DeepLabV3 + , Unet, and LWBNA_Unet models trained for 10 times using (a) ‘callbacks’ function of tensorflow, and (b) training of models for fixed epochs of 500. The best trained model in each case is shown with orange, red, and blue curves. In both the cases, the lightweight model developed in present study surpasses the DeepLabv3 + , and Unet in terms of reproducibility, and generality of FAZ segmentation.