Quantificação de mosquitos Aedes aegypti a partir de imagens de smartphones
Resumo
A vigilância automática do mosquito Aedes aegypti é um desenvolvimento tecnológico com potencial de transformar as atuais práticas de monitoramento. Monitorar mosquitos se traduz em estimar o tamanho da população de mosquitos, isto é, contar a quantidade de mosquitos da espécie alvo dada a região monitorada. Contar é o conceito mais fundamental da matemática e um desafio para o Aprendizado de Máquina. Nesse sentido, a quantificação é uma tarefa de Aprendizado de Máquina recentemente formalizada, cujo objetivo é predizer a distribuição de classes dado um conjunto de teste. Neste trabalho, foram avaliados diferentes quantificadores a partir de imagens de vetores de doenças. Os resultados empíricos demonstram que o método de classificar e contar é um baseline, sendo superado pelos métodos DyS e HDy.
Referências
Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L., Drake, J. M., Brownstein, J. S., Hoen, A. G., Sankoh, O., et al. (2013). The global distribution and burden of dengue. Nature, 496(7446):504–507.
Dietterich, T. G. and Kong, E. B. (1995). Machine learning bias, statistical bias, and statistical variance of decision tree algorithms.
Flach, P. (2012). Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press.
Forman, G. (2006). Quantifying trends accurately despite classifier error and class imbalance. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 157–166.
González, P., Castaño, A., Chawla, N. V., and Coz, J. J. D. (2017). A review on quantification learning. ACM Computing Surveys (CSUR), 50(5):1–40.
González-Castro, V., Alaiz-Rodríguez, R., and Alegre, E. (2013). Class distribution estimation based on the hellinger distance. Information Sciences, 218:146–164.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187:27–48.
Hassan, W., Maletzke, A., and Batista, G. (2020). Accurately quantifying a billion instances per second. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pages 1–10. IEEE.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Maletzke, A., dos Reis, D., Cherman, E., and Batista, G. (2019). Dys: A framework for mixture models in quantification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4552–4560.
Ministério da Saúde (2013). Levantamento rápido de índices para aedes aegypti–liraa para vigilância entomológica do aedes aegypti no brasil: metodologia para avaliação dos índices de breteau e predial e tipo de recipientes.
Ministério da Saúde (2019). Boletim epidemiológico 22. Vol. 50, pp. 1-11.
Park, J., Kim, D. I., Choi, B., Kang, W., and Kwon, H. W. (2020). Classification and morphological analysis of vector mosquitoes using deep convolutional neural networks. Scientific reports, 10(1):1012.
Pezzin, A., Sy, V., Puggioli, A., Veronesi, R., Carrieri, M., Maccagnani, B., and Bellini, R. (2016). Comparative study on the effectiveness of different mosquito traps in arbovirus surveillance with a focus on wnv detection. Acta tropica, 153:93–100.
Pise, R., Patil, K., Laad, M., and Pise, N. (2022). Dataset of vector mosquito images. Data in Brief, 45:108573.
Reed, E. M., Byrd, B. D., Richards, S. L., Eckardt, M., Williams, C., and Reiskind, M. H. (2019). A statewide survey of container aedes mosquitoes (diptera: Culicidae) in north carolina, 2016: a multiagency surveillance response to zika using ovitraps. Journal of medical entomology, 56(2):483–490.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Dietterich, T. G. and Kong, E. B. (1995). Machine learning bias, statistical bias, and statistical variance of decision tree algorithms.
Flach, P. (2012). Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press.
Forman, G. (2006). Quantifying trends accurately despite classifier error and class imbalance. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 157–166.
González, P., Castaño, A., Chawla, N. V., and Coz, J. J. D. (2017). A review on quantification learning. ACM Computing Surveys (CSUR), 50(5):1–40.
González-Castro, V., Alaiz-Rodríguez, R., and Alegre, E. (2013). Class distribution estimation based on the hellinger distance. Information Sciences, 218:146–164.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187:27–48.
Hassan, W., Maletzke, A., and Batista, G. (2020). Accurately quantifying a billion instances per second. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pages 1–10. IEEE.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Maletzke, A., dos Reis, D., Cherman, E., and Batista, G. (2019). Dys: A framework for mixture models in quantification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4552–4560.
Ministério da Saúde (2013). Levantamento rápido de índices para aedes aegypti–liraa para vigilância entomológica do aedes aegypti no brasil: metodologia para avaliação dos índices de breteau e predial e tipo de recipientes.
Ministério da Saúde (2019). Boletim epidemiológico 22. Vol. 50, pp. 1-11.
Park, J., Kim, D. I., Choi, B., Kang, W., and Kwon, H. W. (2020). Classification and morphological analysis of vector mosquitoes using deep convolutional neural networks. Scientific reports, 10(1):1012.
Pezzin, A., Sy, V., Puggioli, A., Veronesi, R., Carrieri, M., Maccagnani, B., and Bellini, R. (2016). Comparative study on the effectiveness of different mosquito traps in arbovirus surveillance with a focus on wnv detection. Acta tropica, 153:93–100.
Pise, R., Patil, K., Laad, M., and Pise, N. (2022). Dataset of vector mosquito images. Data in Brief, 45:108573.
Reed, E. M., Byrd, B. D., Richards, S. L., Eckardt, M., Williams, C., and Reiskind, M. H. (2019). A statewide survey of container aedes mosquitoes (diptera: Culicidae) in north carolina, 2016: a multiagency surveillance response to zika using ovitraps. Journal of medical entomology, 56(2):483–490.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Publicado
10/04/2024
Como Citar
ORTEGA, João Pedro; NADAI, Barbara Lepretti de; MALETZKE, André Gustavo.
Quantificação de mosquitos Aedes aegypti a partir de imagens de smartphones. In: ESCOLA REGIONAL DE BANCO DE DADOS (ERBD), 19. , 2024, Farroupilha/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 61-70.
ISSN 2595-413X.
DOI: https://doi.org/10.5753/erbd.2024.238863.
