Classification of Tropical Disease-carrying Mosquitoes Using Deep Learning and SHAP

  • Vinicius L. N. Fonseca UFAM
  • Fagner Cunha UFAM
  • Larissa Andrade UFAM
  • Juan G. Colonna UFAM
  • David De Yong UNRC


In this paper, we present a novel technique for identifying mosquitoes that carry tropical diseases using Deep Learning and SHAP for model interpretability. We propose an end-to-end deep (E2E) Convolutional Neural Network (CNN) architecture that leverages mosquito wingbeat sounds to extract relevant features. To achieve high-performance audio processing, we integrate Kapre, an audio processing library optimized for GPU execution. Our approach also incorporates SHAP to provide a transparent explanation of the model’s predictions, enabling us to identify and characterize the time-frequency patterns that the model emphasizes. Ultimately, our research aims to support disease control initiatives by providing an automated means of identifying disease-carrying mosquito species, which has the potential to improve public health in tropical regions.


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FONSECA, Vinicius L. N.; CUNHA, Fagner; ANDRADE, Larissa; COLONNA, Juan G.; YONG, David De. Classification of Tropical Disease-carrying Mosquitoes Using Deep Learning and SHAP. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 25-34. ISSN 2763-8952. DOI: