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. 2021 Aug 4;11:15814. doi: 10.1038/s41598-021-95249-3

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© The Author(s) 2021

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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The architecture of Convolutional Neural Network (CNN). CNNs are networks specifically designed to process images. Many small filters compose each CNN layer. A filter is a small matrix of weights that is repeatedly applied to the image pixels. By sharing the filter across the image, repeating patterns are recognized. CNNs are ideal for image analysis since images are composed of repeating patterns. The shallow layers of the CNN recognize low-level patterns. The deeper layers gain a high-level understanding of the image.