Acquisition of digital images and identification of Aedes aegypti mosquito eggs using classification and deep learning.

  • Pedro Saint Garcia Federal University of Ouro Preto
  • Rafael Martins Federal University of Ouro Preto
  • George Coelho Federal University of Ouro Preto
  • Guillermo Camara-Chavez Federal University of Ouro Preto

Resumo


The mosquito Aedes aegypti can transmit some diseases, which makes the study of the proliferation of this vector a necessary task. With the use of traps made in the laboratory, called ovitraps, it is possible to map egg deposition in a community. Through a camera, coupled with a magnifying glass are acquired images containing the elements (eggs) to be counted. First, the goal is to find pixels with a similar color to mosquito eggs, for that, we take advantage of the slice color method. From these already worked images, a process of transfer learning with a convolutional neural network (CNN) is carried out. The intention is to separate which elements are actually eggs from the others. In 10\% of the test images, the count performed by the model in relation to the actual number of eggs was considered to be weakly correlated, this occurs in images that have a high density of eggs or appear black elements that resemble mosquito eggs, but they are not. For the remaining 90\% of test images, the counting was considered to be perfectly correlated.

Palavras-chave: Aedes aegypti egg counting, mosquito eggs, deep learning

Referências

Centers for disease control and prevention. yellow book: 20Health information for international travel, New York:Oxford University, 2018.

I. A. Lobo, Dengue fever, cambridge, ma:Npg education. nature, June 20[online] Available: https://www.nature.com/scitable/ebooks/dengue-fever-22453392.

Dengue control, November 20[online] Available: http://www.who.int/denguecontrol/mosquito/en/.

M. Teixeira, M. Barreto, Z. Guerra, "Epidemiologia e medidas de prevenção do dengue", IESUS, vol. 8, no. 1999.

A. Pedro, "O dengue em nictheroy", Brazil-Mé dico, vol. 1, 1923.

C. H. Osanai, A epidemia de dengue em boa vista território federal de roraima 1981–191984.

R. L. d. O. Rotraut, A. G. B. Consoli, Principais mosquitos de im-portá ncia sanitá ria no Brasil. FIOCRUZ, 1994.

O. Forattini, "Ecologia epidemiologia e sociedade", Editora Artes Mé dicas EDUSP - sã o Paulo, 1992.

K. M. Pepin, C. Marques-Toledo, L. Scherer, M. M. Morais, B. Ellis, A. E, "Cost-effectiveness of novel system of mosquito surveillance and control brazil", Emerg Infect Dis, 1914.

K. B. F. Marzochi, "Dengue in brazil - situation transmission and control: a proposal for ecological control", SciELO Analytics, 2013.

K. G. Luz, G. I. V. dos Santos, R. de Magalhães Vieira, "Febre pelo vírus zika", Journal of General Virology, vol. 2, 2015.

M. R. Donalisio, A. R. R. Freitas, "Chikungunya no brasil: um desafio emergente", Journal of General Virology, 2014.

R. W. Fay, A. S. Perry, "Laboratory studies of ovipositional preferences of aedes aegypti", Mosquito News, vol. pp. 276-21965.

"Avaliação de armadilhas para a vigilância entomológica de aedes aegypti com vistas á elaboração de novos índices de infestação", NOTA TECNICA N.° 312014/IOC-FIOCRUZ/DIRETORIA, 2014.

C. A. B. Mello, "Automatic counting of aedes aegypti eggs in images of ovitraps", Ganesh R Naik (Ed.), 2009.

M. N. B. Portela, "Contagem automática de ovos de aedes aegypti em imagens de ovitrampas", Escola Polité cnica de Pernambuco, 2009.

G. G. ao, "A new algorithm for segmenting and counting aedes aegypti eggs in ovitraps", 31st Annual International Conference of the IEEE EMBS Minneapolis, 2009.

J. Gaburro, "Assessment of icount software a precise and fast egg counting tool for the mosquito vector aedes aegypti", Parasites e Vectors, 2016.

A. Mollahosseini, "Auser-friendlysoftwaretoeasilycount anophelesegg-batches", BioMed Central, 2012.

A. Krizhevsky, I. Sutskever, G. E. Hinton, F. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger, "Imagenet classification with deep convolutional neural networks", Advances in Neural Information Processing Systems pp. 1097-1105, 2012.

J. Hung, A. Goodman, S. Lopes, G. Rangel, D. Ravel, F. Costa, M. Duraisingh, M. Marti, A. Carpenter, "Applying faster r-cnn for object detection on malaria images", Vis Pattern Recognit Workshops, 2018.

J. A. Quinn, R. Nakasi, P. K. B. Mugagga, P. Byanyima, W. Lubega, A. Andama, Deep convolutional neural networks for microscopy-based point of care diagnostics, 2016.

P. V. V. Paiva, "Contagem automática de ovos do carrapato rhipicephalus (boophilus) microplus em imagens microscópicas", Universidade Ged-eral de Alagoas, 2015.

J. W. Mains, D. R. Mercer, S. L. Dobson, "Digital image analysis to estimate numbers of aedes eggs oviposited in containers", The American Mosquito Control Association, 2008.

Deep learning example: Traning from scratch using cifar-10 dataset.matlab mathworks. [Online], 20[online] Available: https://www.mathworks.com/examples/matlab/community/36291-deep-learning-example-traning-from-scratch-using-cifar-10-dataset.

R. C. Gonzalez, Digital Image Processing, Inc:Pearson Education, 2008.

N. Otsu, "A threshold selection method from gray level histograms", Automatica, vol. no. 296, pp. 23-1975.

C. Qi, "Maximum entropy for image segmentation based on an adaptive particle swarm optimization", Applied Mathematics & Information Sciences, 2014.

S. M. B. B. Correa, "Probabilidade e Estatí stica", Pontifícia Universidade Católica de Minas Gerais, 2006.

R. Real, J. M. Vargas, "The probabilistic basis of jaccard's index of similarity", Department of Animal Biology Faculty of Science University of Malaga Malaga 29071 Spain, 1996.

J. Davis, M. Goadrich, "The relationship between precision-recall and roc curves", Department of Computer Sciences and Department of Biostatistics and Medical Informatics University of Wisconsin-Madison 1West Dayton Street Madison WI 53USA, 2006.
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28/10/2019
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GARCIA, Pedro Saint; MARTINS, Rafael; COELHO, George; CAMARA-CHAVEZ, Guillermo. Acquisition of digital images and identification of Aedes aegypti mosquito eggs using classification and deep learning.. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . DOI: https://doi.org/10.5753/sibgrapi.2019.9777.