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Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors
Khalighifar, Ali Komp, Ed Ramsey, Janine M. Gurgel-Gonçalves, Rodrigo Peterson, A. Townsend
Khalighifar, Ali
Komp, Ed
Ramsey, Janine M.
Gurgel-Gonçalves, Rodrigo
Peterson, A. Townsend
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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.
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
Date
2019-05-23
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Publisher
Oxford University Press
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Keywords
Chagas disease, TensorFlow, Deep learning, Triatominae, Automated species identification
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