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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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Variations in training and validation dice coefficient (a) for DeepLabv3 + , (b) Unet, and (c) LWBNA_Unet trained for the fixed 500 epochs and stopped early by using callbacks function to avoid overfitting of the model. Each model was trained for 10 times. Here, we show the training/validation curves for the model, which produced lowest variations (out of 10 times training) in D for each case. Inset to (a) shows the variation in D, when the Resnet-50 encoder of DeepLabV3 + used pretrained weights of Imagenet dataset.