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A Swin Transformer-based model for mosquito species identification
Mosquito transmit numbers of parasites and pathogens resulting in fatal diseases. Species identification is a prerequisite for effective mosquito control. Existing morphological and molecular classification methods have evitable disadvantages. Here we introduced Deep learning techniques for mosquito...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636261/ https://www.ncbi.nlm.nih.gov/pubmed/36333318 http://dx.doi.org/10.1038/s41598-022-21017-6 |
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author | Zhao, De-zhong Wang, Xin-kai Zhao, Teng Li, Hu Xing, Dan Gao, He-ting Song, Fan Chen, Guo-hua Li, Chun-xiao |
author_facet | Zhao, De-zhong Wang, Xin-kai Zhao, Teng Li, Hu Xing, Dan Gao, He-ting Song, Fan Chen, Guo-hua Li, Chun-xiao |
author_sort | Zhao, De-zhong |
collection | PubMed |
description | Mosquito transmit numbers of parasites and pathogens resulting in fatal diseases. Species identification is a prerequisite for effective mosquito control. Existing morphological and molecular classification methods have evitable disadvantages. Here we introduced Deep learning techniques for mosquito species identification. A balanced, high-definition mosquito dataset with 9900 original images covering 17 species was constructed. After three rounds of screening and adjustment-testing (first round among 3 convolutional neural networks and 3 Transformer models, second round among 3 Swin Transformer variants, and third round between 2 images sizes), we proposed the first Swin Transformer-based mosquito species identification model (Swin MSI) with 99.04% accuracy and 99.16% F1-score. By visualizing the identification process, the morphological keys used in Swin MSI were similar but not the same as those used by humans. Swin MSI realized 100% subspecies-level identification in Culex pipiens Complex and 96.26% accuracy for novel species categorization. It presents a promising approach for mosquito identification and mosquito borne diseases control. |
format | Online Article Text |
id | pubmed-9636261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96362612022-11-06 A Swin Transformer-based model for mosquito species identification Zhao, De-zhong Wang, Xin-kai Zhao, Teng Li, Hu Xing, Dan Gao, He-ting Song, Fan Chen, Guo-hua Li, Chun-xiao Sci Rep Article Mosquito transmit numbers of parasites and pathogens resulting in fatal diseases. Species identification is a prerequisite for effective mosquito control. Existing morphological and molecular classification methods have evitable disadvantages. Here we introduced Deep learning techniques for mosquito species identification. A balanced, high-definition mosquito dataset with 9900 original images covering 17 species was constructed. After three rounds of screening and adjustment-testing (first round among 3 convolutional neural networks and 3 Transformer models, second round among 3 Swin Transformer variants, and third round between 2 images sizes), we proposed the first Swin Transformer-based mosquito species identification model (Swin MSI) with 99.04% accuracy and 99.16% F1-score. By visualizing the identification process, the morphological keys used in Swin MSI were similar but not the same as those used by humans. Swin MSI realized 100% subspecies-level identification in Culex pipiens Complex and 96.26% accuracy for novel species categorization. It presents a promising approach for mosquito identification and mosquito borne diseases control. Nature Publishing Group UK 2022-11-04 /pmc/articles/PMC9636261/ /pubmed/36333318 http://dx.doi.org/10.1038/s41598-022-21017-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhao, De-zhong Wang, Xin-kai Zhao, Teng Li, Hu Xing, Dan Gao, He-ting Song, Fan Chen, Guo-hua Li, Chun-xiao A Swin Transformer-based model for mosquito species identification |
title | A Swin Transformer-based model for mosquito species identification |
title_full | A Swin Transformer-based model for mosquito species identification |
title_fullStr | A Swin Transformer-based model for mosquito species identification |
title_full_unstemmed | A Swin Transformer-based model for mosquito species identification |
title_short | A Swin Transformer-based model for mosquito species identification |
title_sort | swin transformer-based model for mosquito species identification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636261/ https://www.ncbi.nlm.nih.gov/pubmed/36333318 http://dx.doi.org/10.1038/s41598-022-21017-6 |
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