Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses

Antonio Arista-Jalife; Alejandra Sanchez; Mariko Nakano; Henrik Tünnermann; Hector Perez-Meana; Hayaru Shouno

doi:10.3233/978-1-61499-900-3-108

Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses

Antonio Arista-Jalife, Alejandra Sanchez, Mariko Nakano, Henrik Tünnermann, Hector Perez-Meana, Hayaru Shouno

Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Unidad Culhuacán

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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Resumen

In this paper, a novel Deep Neural Network topology is presented with the objective of recognizing the Aedes aegypti and Aedes albopictus mosquito in their larvarian stage, which are the vectors that cause Dengue, Chikungunya, Zika and Yellow Fever outbreaks. This solution allows to determine if a sample image is a larva of the Aedes aegypti or Aedes albopictus mosquito with an accuracy of 91.28%, a true positive rate of 94.18% and a true negative rate of 88.37%. This Deep Neural Network topology allows the implementation of fast and accurate preventive measures in under-developed countries and isolated areas where a trained specialist might not be available.

Idioma original	Inglés
Título de la publicación alojada	New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018
Editores	Enrique Herrera-Viedma, Hamido Fujita
Editorial	IOS Press
Páginas	108-120
Número de páginas	13
ISBN (versión digital)	9781614998990
DOI	https://doi.org/10.3233/978-1-61499-900-3-108
Estado	Publicada - 2018
Evento	17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018 - Granada, Espana Duración: 26 sep. 2018 → 28 sep. 2018

Serie de la publicación

Nombre	Frontiers in Artificial Intelligence and Applications
Volumen	303
ISSN (versión impresa)	0922-6389

Conferencia

Conferencia	17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018
País/Territorio	Espana
Ciudad	Granada
Período	26/09/18 → 28/09/18

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Arista-Jalife, A., Sanchez, A., Nakano, M., Tünnermann, H., Perez-Meana, H., & Shouno, H. (2018). Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses. En E. Herrera-Viedma, & H. Fujita (Eds.), New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018 (pp. 108-120). (Frontiers in Artificial Intelligence and Applications; Vol. 303). IOS Press. https://doi.org/10.3233/978-1-61499-900-3-108

Arista-Jalife, Antonio ; Sanchez, Alejandra ; Nakano, Mariko et al. / Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses. New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018. editor / Enrique Herrera-Viedma ; Hamido Fujita. IOS Press, 2018. pp. 108-120 (Frontiers in Artificial Intelligence and Applications).

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title = "Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses",

abstract = "In this paper, a novel Deep Neural Network topology is presented with the objective of recognizing the Aedes aegypti and Aedes albopictus mosquito in their larvarian stage, which are the vectors that cause Dengue, Chikungunya, Zika and Yellow Fever outbreaks. This solution allows to determine if a sample image is a larva of the Aedes aegypti or Aedes albopictus mosquito with an accuracy of 91.28%, a true positive rate of 94.18% and a true negative rate of 88.37%. This Deep Neural Network topology allows the implementation of fast and accurate preventive measures in under-developed countries and isolated areas where a trained specialist might not be available.",

keywords = "Aedes mosquito, Chikungunya, Convolutional Neural Networks, Data augmentation, Deep Learning, Dengue, Entomology, Transfer Learning, Vector identification, Zika",

author = "Antonio Arista-Jalife and Alejandra Sanchez and Mariko Nakano and Henrik T{\"u}nnermann and Hector Perez-Meana and Hayaru Shouno",

note = "Publisher Copyright: {\textcopyright} 2018 The authors and IOS Press. All rights reserved.; 17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018 ; Conference date: 26-09-2018 Through 28-09-2018",

year = "2018",

doi = "10.3233/978-1-61499-900-3-108",

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series = "Frontiers in Artificial Intelligence and Applications",

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Arista-Jalife, A, Sanchez, A, Nakano, M, Tünnermann, H, Perez-Meana, H & Shouno, H 2018, Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses. En E Herrera-Viedma & H Fujita (eds.), New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018. Frontiers in Artificial Intelligence and Applications, vol. 303, IOS Press, pp. 108-120, 17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018, Granada, Espana, 26/09/18. https://doi.org/10.3233/978-1-61499-900-3-108

Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses. / Arista-Jalife, Antonio; Sanchez, Alejandra; Nakano, Mariko et al.
New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018. ed. / Enrique Herrera-Viedma; Hamido Fujita. IOS Press, 2018. p. 108-120 (Frontiers in Artificial Intelligence and Applications; Vol. 303).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses

AU - Arista-Jalife, Antonio

AU - Sanchez, Alejandra

AU - Nakano, Mariko

AU - Tünnermann, Henrik

AU - Perez-Meana, Hector

AU - Shouno, Hayaru

PY - 2018

Y1 - 2018

N2 - In this paper, a novel Deep Neural Network topology is presented with the objective of recognizing the Aedes aegypti and Aedes albopictus mosquito in their larvarian stage, which are the vectors that cause Dengue, Chikungunya, Zika and Yellow Fever outbreaks. This solution allows to determine if a sample image is a larva of the Aedes aegypti or Aedes albopictus mosquito with an accuracy of 91.28%, a true positive rate of 94.18% and a true negative rate of 88.37%. This Deep Neural Network topology allows the implementation of fast and accurate preventive measures in under-developed countries and isolated areas where a trained specialist might not be available.

AB - In this paper, a novel Deep Neural Network topology is presented with the objective of recognizing the Aedes aegypti and Aedes albopictus mosquito in their larvarian stage, which are the vectors that cause Dengue, Chikungunya, Zika and Yellow Fever outbreaks. This solution allows to determine if a sample image is a larva of the Aedes aegypti or Aedes albopictus mosquito with an accuracy of 91.28%, a true positive rate of 94.18% and a true negative rate of 88.37%. This Deep Neural Network topology allows the implementation of fast and accurate preventive measures in under-developed countries and isolated areas where a trained specialist might not be available.

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KW - Convolutional Neural Networks

KW - Data augmentation

KW - Deep Learning

KW - Dengue

KW - Entomology

KW - Transfer Learning

KW - Vector identification

KW - Zika

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M3 - Contribución a la conferencia

T3 - Frontiers in Artificial Intelligence and Applications

SP - 108

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BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018

A2 - Herrera-Viedma, Enrique

A2 - Fujita, Hamido

PB - IOS Press

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Y2 - 26 September 2018 through 28 September 2018

ER -

Arista-Jalife A, Sanchez A, Nakano M, Tünnermann H, Perez-Meana H, Shouno H. Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses. En Herrera-Viedma E, Fujita H, editores, New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018. IOS Press. 2018. p. 108-120. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/978-1-61499-900-3-108

Deep Learning employed in the recognition of the vector that spreads dengue, chikungunya and Zika viruses

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