Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

Abstract 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 met...

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Autores principales:	Adam Goodwin, Sanket Padmanabhan, Sanchit Hira, Margaret Glancey, Monet Slinowsky, Rakhil Immidisetti, Laura Scavo, Jewell Brey, Bala Murali Manoghar Sai Sudhakar, Tristan Ford, Collyn Heier, Yvonne-Marie Linton, David B. Pecor, Laura Caicedo-Quiroga, Soumyadipta Acharya
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e21bc90183f8402585e3f54b7e9d49ad
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spelling	oai:doaj.org-article:e21bc90183f8402585e3f54b7e9d49ad2021-12-02T18:18:58ZMosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection10.1038/s41598-021-92891-92045-2322https://doaj.org/article/e21bc90183f8402585e3f54b7e9d49ad2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92891-9https://doaj.org/toc/2045-2322Abstract 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.Adam GoodwinSanket PadmanabhanSanchit HiraMargaret GlanceyMonet SlinowskyRakhil ImmidisettiLaura ScavoJewell BreyBala Murali Manoghar Sai SudhakarTristan FordCollyn HeierYvonne-Marie LintonDavid B. PecorLaura Caicedo-QuirogaSoumyadipta AcharyaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Adam Goodwin Sanket Padmanabhan Sanchit Hira Margaret Glancey Monet Slinowsky Rakhil Immidisetti Laura Scavo Jewell Brey Bala Murali Manoghar Sai Sudhakar Tristan Ford Collyn Heier Yvonne-Marie Linton David B. Pecor Laura Caicedo-Quiroga Soumyadipta Acharya Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection
description	Abstract 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	article
author	Adam Goodwin Sanket Padmanabhan Sanchit Hira Margaret Glancey Monet Slinowsky Rakhil Immidisetti Laura Scavo Jewell Brey Bala Murali Manoghar Sai Sudhakar Tristan Ford Collyn Heier Yvonne-Marie Linton David B. Pecor Laura Caicedo-Quiroga Soumyadipta Acharya
author_facet	Adam Goodwin Sanket Padmanabhan Sanchit Hira Margaret Glancey Monet Slinowsky Rakhil Immidisetti Laura Scavo Jewell Brey Bala Murali Manoghar Sai Sudhakar Tristan Ford Collyn Heier Yvonne-Marie Linton David B. Pecor Laura Caicedo-Quiroga Soumyadipta Acharya
author_sort	Adam Goodwin
title	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_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_sort	mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e21bc90183f8402585e3f54b7e9d49ad
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Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

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