Mosquito species identification using convolutional neural networks with a multitiered ensemble mode...
Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection
About this item
Full title
Author / Creator
Goodwin, Autumn , 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 and Acharya, Soumyadipta
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
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 wi...
Alternative Titles
Full title
Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_e21bc90183f8402585e3f54b7e9d49ad
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e21bc90183f8402585e3f54b7e9d49ad
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-92891-9
