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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-92891-9 |
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| author | Autumn 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 | Autumn 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 | Autumn Goodwin |
| collection | DOAJ |
| 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 |
| id | doaj-art-e21bc90183f8402585e3f54b7e9d49ad |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e21bc90183f8402585e3f54b7e9d49ad2025-08-20T02:15:08ZengNature PortfolioScientific Reports2045-23222021-07-0111111510.1038/s41598-021-92891-9Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detectionAutumn Goodwin0Sanket Padmanabhan1Sanchit Hira2Margaret Glancey3Monet Slinowsky4Rakhil Immidisetti5Laura Scavo6Jewell Brey7Bala Murali Manoghar Sai Sudhakar8Tristan Ford9Collyn Heier10Yvonne-Marie Linton11David B. Pecor12Laura Caicedo-Quiroga13Soumyadipta Acharya14VectechVectechCenter for Bioengineering Innovation and Design, Biomedical Engineering Department, Whiting School of Engineering, The Johns Hopkins UniversityVectechCenter for Bioengineering Innovation and Design, Biomedical Engineering Department, Whiting School of Engineering, The Johns Hopkins UniversityCenter for Bioengineering Innovation and Design, Biomedical Engineering Department, Whiting School of Engineering, The Johns Hopkins UniversityCenter for Bioengineering Innovation and Design, Biomedical Engineering Department, Whiting School of Engineering, The Johns Hopkins UniversityCenter for Bioengineering Innovation and Design, Biomedical Engineering Department, Whiting School of Engineering, The Johns Hopkins UniversityVectechVectechCenter for Bioengineering Innovation and Design, Biomedical Engineering Department, Whiting School of Engineering, The Johns Hopkins UniversityWalter Reed Biosystematics Unit (WRBU), Smithsonian Institution Museum Support CenterWalter Reed Biosystematics Unit (WRBU), Smithsonian Institution Museum Support CenterWalter Reed Biosystematics Unit (WRBU), Smithsonian Institution Museum Support CenterCenter for Bioengineering Innovation and Design, Biomedical Engineering Department, Whiting School of Engineering, The Johns Hopkins UniversityAbstract 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.https://doi.org/10.1038/s41598-021-92891-9 |
| spellingShingle | Autumn 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 Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-021-92891-9 |
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