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. 2022 Mar 14;12:4329. doi: 10.1038/s41598-022-07890-1

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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 representation of the CNN architecture used. Actual model is volumetric, i.e. three spatial dimensions plus a channels dimension. Green arrows represent convolution operations with stride of 1. A ReLU nonlinear activation is applied after convolutions, and then a 2×2×2 max pooling in order to reduce spatial dimensions. Red arrow represents flattening. Blue arrows are full connections (with a 0.25 dropout), purple arrow stands for the final classifier with Log SoftMax and Cross Entropy loss function.