dc.contributor.author | Khalighifar, Ali | |
dc.contributor.author | Komp, Ed | |
dc.contributor.author | Ramsey, Janine M. | |
dc.contributor.author | Gurgel-Gonçalves, Rodrigo | |
dc.contributor.author | Peterson, A. Townsend | |
dc.date.accessioned | 2019-08-13T23:02:38Z | |
dc.date.available | 2019-08-13T23:02:38Z | |
dc.date.issued | 2019-05-23 | |
dc.identifier.citation | Ali Khalighifar, Ed Komp, Janine M Ramsey, Rodrigo Gurgel-Gonçalves, A Townsend Peterson, Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors, Journal of Medical Entomology, , tjz065, https://doi.org/10.1093/jme/tjz065 | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/29437 | |
dc.description | This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Medical Entomology following peer review. The version of record is available online at: https;//doi.org/https://doi.org/10.1093/jme/tjz065 | en_US |
dc.description.abstract | Vector-borne Chagas disease is endemic to the Americas and imposes significant economic and social burdens on public health. In a previous contribution, we presented an automated identification system that was able to discriminate among 12 Mexican and 39 Brazilian triatomine (Hemiptera: Reduviidae) species from digital images. To explore the same data more deeply using machine-learning approaches, hoping for improvements in classification, we employed TensorFlow, an open-source software platform for a deep learning algorithm. We trained the algorithm based on 405 images for Mexican triatomine species and 1,584 images for Brazilian triatomine species. Our system achieved 83.0 and 86.7% correct identification rates across all Mexican and Brazilian species, respectively, an improvement over comparable rates from statistical classifiers (80.3 and 83.9%, respectively). Incorporating distributional information to reduce numbers of species in analyses improved identification rates to 95.8% for Mexican species and 98.9% for Brazilian species. Given the ‘taxonomic impediment’ and difficulties in providing entomological expertise necessary to control such diseases, automating the identification process offers a potential partial solution to crucial challenges. | en_US |
dc.publisher | Oxford University Press | en_US |
dc.rights | © The Author(s) 2019. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. | en_US |
dc.subject | Chagas disease | en_US |
dc.subject | TensorFlow | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Triatominae | en_US |
dc.subject | Automated species identification | en_US |
dc.title | Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Khalighifar, Ali | |
kusw.kuauthor | Peterson, A. Townsend | |
kusw.kudepartment | Ecology & Evolutionary Biology | en_US |
dc.identifier.doi | 10.1093/jme/tjz065 | en_US |
kusw.oaversion | Scholarly/refereed, author accepted manuscript | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.rights.accessrights | OpenAccess | en_US |