© 2019 Elsevier B.V. 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.
Arista-Jalife, A., Nakano, M., Garcia-Nonoal, Z., Robles-Camarillo, D., Perez-Meana, H., & Arista-Viveros, H. A. (2020). Aedes mosquito detection in its larval stage using deep neural networks. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2019.07.012