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. 2020 Jan 23;10:1012. doi: 10.1038/s41598-020-57875-1

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© The Author(s) 2020

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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The flow of the classification and visualization in the VGG-16 DCNN model. The class of a given mosquito image is predicted by two steps: (1) extracting hierarchical features and (2) classifying these features. In the feature extractor part, feature maps generated by filters at each convolution layer are shown. These feature maps are used for visualization by weighting them with channel-wise averaged gradients.

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