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Development of a Deep Learning Model for the Classification of Mosquito Larvae Images

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14197))

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Abstract

Dengue is a disease that is endemic to certain regions, and in 2022, it was responsible for more than three million cases in the Americas. One of the most effective ways to prevent Dengue is by preventing the formation of breeding sites for the Aedes aegypti mosquito, which is the primary vector of the disease. Unfortunately, identifying these breeding sites remains a challenge as citizens lack knowledge to distinguish Ae. Aegypti larvae from other species. A solution can be the development of a Deep Learning model, to be deployed in a mobile application that classifies mosquito species using photos of larvae. Currently only a few models are available that mostly differentiate only between genera (Aedes versus non-Aedes), or present very low accuracy. Therefore, the objective of this research is to develop an image classification model that can differentiate between Ae. Aegypti, Aedes albopictus, and Culex sp. Larvae using pictures taken with a cellphone camera by comparing various Deep Learning models (Mobilenetv2, ResNet18, ResNet34, EfficientNet_B0 and EfficientNet_Lite0). Best results were obtained with EfficientNet_Lite0 with an accuracy of 97.5% during validation and 90% during testing, an acceptable result considering the risks related to a misclassification in this context. These results demonstrate the viability of a classification of mosquito larvae differentiating even between Aedes species and thus providing a contribution to the prevention of dengue.

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Notes

  1. 1.

    The dataset and notebook for the model are being prepared for availability.

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Acknowledgments

This work was supported by the CNPq (National Council for Scientific and Technological Development), a Brazilian government entity focused on scientific and technological development.

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Authors and Affiliations

  1. Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, SC, 88040-900, Brazil

    Ramon Mayor Martins, Bruno Manarin Espíndola, Pedro Philippi Araujo & Christiane Gresse von Wangenheim

  2. Department of Microbiology, Immunology and Parasitology/Center for Biological Sciences, Federal University of Santa Catarina, Florianópolis, SC, 88040-900, Brazil

    Carlos José de Carvalho Pinto

  3. Coordination of the Laboratory Reference Network, Santa Catarina, Florianópolis, SC, 88010-001, Brazil

    Gisele Caminha

Authors
  1. Ramon Mayor Martins
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  2. Bruno Manarin Espíndola
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  3. Pedro Philippi Araujo
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  4. Christiane Gresse von Wangenheim
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  5. Carlos José de Carvalho Pinto
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  6. Gisele Caminha
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Corresponding author

Correspondence to Ramon Mayor Martins .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Martins, R.M., Espíndola, B.M., Araujo, P.P., von Wangenheim, C.G., de Carvalho Pinto, C.J., Caminha, G. (2023). Development of a Deep Learning Model for the Classification of Mosquito Larvae Images. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-45392-2_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-45392-2

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