TY - JOUR
T1 - Aedes mosquito detection in its larval stage using deep neural networks
AU - Arista-Jalife, Antonio
AU - Nakano, Mariko
AU - Garcia-Nonoal, Zaira
AU - Robles-Camarillo, Daniel
AU - Perez-Meana, Hector
AU - Arista-Viveros, Heriberto Antonio
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/2/15
Y1 - 2020/2/15
N2 - Dengue, Chikungunya and Zika viruses cause dangerous infections in tropical and subtropical regions throughout the world. The World Health Organization estimates that one out of every three persons in the entire human population is in danger of contracting one of these diseases from a single mosquito bite. Currently, these viral infections are not preventable by vaccines and there is not a direct treatment that can effectively diminish the viral infection, which causes a wide range of pathologies, including severe joint pain, internal blood loss, permanent neurological damage in unborn children and even death. Due to this grim scenario, the best and maybe the only line of defense against these diseases is the effective surveillance, control and suppression of the mosquitoes that transmit these viruses: Aedes aegypti and Aedes albopictus. In this paper, we present a complete solution that is capable of identifying the Aedes aegypti and Aedes albopictus mosquito in the larval stage, which is easily disposable, restricted to water bodies, and incapable of transmitting diseases according to the Centers for Disease Control and Prevention (CDC). Our proposal is based on deep neural networks (DNN) that effectively recognize larval samples with an accuracy of 94.19%, which is better than other state-of-the-art automatic methods. Additionally, the capabilities of our proposed DNN allow us to automatically crop the region of interest (ROI) with an accuracy of 92.85% and then automatically classify the region as Aedes positive or Aedes negative, without further human intervention and in less than a second, accelerating the response time for biological control from days to seconds. Our proposal includes hardware designs that allow inexpensive implementation, making it suitable for isolated communities, underdeveloped countries, and rural or urban areas.
AB - Dengue, Chikungunya and Zika viruses cause dangerous infections in tropical and subtropical regions throughout the world. The World Health Organization estimates that one out of every three persons in the entire human population is in danger of contracting one of these diseases from a single mosquito bite. Currently, these viral infections are not preventable by vaccines and there is not a direct treatment that can effectively diminish the viral infection, which causes a wide range of pathologies, including severe joint pain, internal blood loss, permanent neurological damage in unborn children and even death. Due to this grim scenario, the best and maybe the only line of defense against these diseases is the effective surveillance, control and suppression of the mosquitoes that transmit these viruses: Aedes aegypti and Aedes albopictus. In this paper, we present a complete solution that is capable of identifying the Aedes aegypti and Aedes albopictus mosquito in the larval stage, which is easily disposable, restricted to water bodies, and incapable of transmitting diseases according to the Centers for Disease Control and Prevention (CDC). Our proposal is based on deep neural networks (DNN) that effectively recognize larval samples with an accuracy of 94.19%, which is better than other state-of-the-art automatic methods. Additionally, the capabilities of our proposed DNN allow us to automatically crop the region of interest (ROI) with an accuracy of 92.85% and then automatically classify the region as Aedes positive or Aedes negative, without further human intervention and in less than a second, accelerating the response time for biological control from days to seconds. Our proposal includes hardware designs that allow inexpensive implementation, making it suitable for isolated communities, underdeveloped countries, and rural or urban areas.
KW - Automatic segmentation
KW - Deep neural networks
KW - Dengue fever
KW - Mosquito larval surveillance
KW - Vector control
KW - Zika fever
UR - http://www.scopus.com/inward/record.url?scp=85069596766&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2019.07.012
DO - 10.1016/j.knosys.2019.07.012
M3 - Artículo
SN - 0950-7051
VL - 189
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 104841
ER -