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Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection
With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Con...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249627/ https://www.ncbi.nlm.nih.gov/pubmed/34211009 http://dx.doi.org/10.1038/s41598-021-92891-9 |
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author | Goodwin, Adam Padmanabhan, Sanket Hira, Sanchit Glancey, Margaret Slinowsky, Monet Immidisetti, Rakhil Scavo, Laura Brey, Jewell Sai Sudhakar, Bala Murali Manoghar Ford, Tristan Heier, Collyn Linton, Yvonne-Marie Pecor, David B. Caicedo-Quiroga, Laura Acharya, Soumyadipta |
author_facet | Goodwin, Adam Padmanabhan, Sanket Hira, Sanchit Glancey, Margaret Slinowsky, Monet Immidisetti, Rakhil Scavo, Laura Brey, Jewell Sai Sudhakar, Bala Murali Manoghar Ford, Tristan Heier, Collyn Linton, Yvonne-Marie Pecor, David B. Caicedo-Quiroga, Laura Acharya, Soumyadipta |
author_sort | Goodwin, Adam |
collection | PubMed |
description | With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region. |
format | Online Article Text |
id | pubmed-8249627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82496272021-07-06 Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection Goodwin, Adam Padmanabhan, Sanket Hira, Sanchit Glancey, Margaret Slinowsky, Monet Immidisetti, Rakhil Scavo, Laura Brey, Jewell Sai Sudhakar, Bala Murali Manoghar Ford, Tristan Heier, Collyn Linton, Yvonne-Marie Pecor, David B. Caicedo-Quiroga, Laura Acharya, Soumyadipta Sci Rep Article With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region. Nature Publishing Group UK 2021-07-01 /pmc/articles/PMC8249627/ /pubmed/34211009 http://dx.doi.org/10.1038/s41598-021-92891-9 Text en © The Author(s) 2021 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 Goodwin, Adam Padmanabhan, Sanket Hira, Sanchit Glancey, Margaret Slinowsky, Monet Immidisetti, Rakhil Scavo, Laura Brey, Jewell Sai Sudhakar, Bala Murali Manoghar Ford, Tristan Heier, Collyn Linton, Yvonne-Marie Pecor, David B. Caicedo-Quiroga, Laura Acharya, Soumyadipta Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection |
title | Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection |
title_full | Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection |
title_fullStr | Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection |
title_full_unstemmed | Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection |
title_short | Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection |
title_sort | mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249627/ https://www.ncbi.nlm.nih.gov/pubmed/34211009 http://dx.doi.org/10.1038/s41598-021-92891-9 |
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