ATTENTION: The software behind KU ScholarWorks is being upgraded to a new version. Starting July 15th, users will not be able to log in to the system, add items, nor make any changes until the new version is in place at the end of July. Searching for articles and opening files will continue to work while the system is being updated.
If you have any questions, please contact Marianne Reed at mreed@ku.edu .
Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors
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 |