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dc.contributor.authorKhalighifar, Ali
dc.contributor.authorKomp, Ed
dc.contributor.authorRamsey, Janine M.
dc.contributor.authorGurgel-Gonçalves, Rodrigo
dc.contributor.authorPeterson, A. Townsend
dc.date.accessioned2019-08-13T23:02:38Z
dc.date.available2019-08-13T23:02:38Z
dc.date.issued2019-05-23
dc.identifier.citationAli 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/tjz065en_US
dc.identifier.urihttp://hdl.handle.net/1808/29437
dc.descriptionThis 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/tjz065en_US
dc.description.abstractVector-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.publisherOxford University Pressen_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.subjectChagas diseaseen_US
dc.subjectTensorFlowen_US
dc.subjectDeep learningen_US
dc.subjectTriatominaeen_US
dc.subjectAutomated species identificationen_US
dc.titleDeep Learning Algorithms Improve Automated Identification of Chagas Disease Vectorsen_US
dc.typeArticleen_US
kusw.kuauthorKhalighifar, Ali
kusw.kuauthorPeterson, A. Townsend
kusw.kudepartmentEcology & Evolutionary Biologyen_US
dc.identifier.doi10.1093/jme/tjz065en_US
kusw.oaversionScholarly/refereed, author accepted manuscripten_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsembargoedAccessen_US


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