Artificial Neural Network applied as a methodology of mosquito species identification
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Data
2015-12-01
Autores
Lorenz, Camila
Ferraudo, Antonio Sergio [UNESP]
Suesdek, Lincoln
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Resumo
There are about 200 species of mosquitoes (Culicidae) known to be vectors of pathogens that cause diseases in humans. Correct identification of mosquito species is an essential step in the development of effective control strategies for these diseases; recognizing the vectors of pathogens is integral to understanding transmission. Unfortunately, taxonomic identification of mosquitoes is a laborious task, which requires trained experts, and it is jeopardized by the high variability of morphological and molecular characters found within the Culicidae family. In this context, the development of an automatized species identification method would be a valuable and more accessible resource to non-taxonomist and health professionals. In this work, an artificial neural network (ANN) technique was proposed for the identification and classification of 17 species of the genera Anopheles, Aedes, and Culex, based on wing shape characters. We tested the hypothesis that classification using ANN is better than traditional classification by discriminant analysis (DA). Thirty-two wing shape principal components were used as input to a Multilayer Perceptron Classification ANN. The obtained ANN correctly identified species with accuracy rates ranging from 85.70% to 100%, and classified species more efficiently than did the traditional method of multivariate discriminant analysis. The results highlight the power of ANNs to diagnose mosquito species and to partly automatize taxonomic identification. These findings also support the hypothesis that wing venation patterns are species-specific, and thus should be included in taxonomic keys. (C) 2015 Elsevier B.V. All rights reserved.
Descrição
Palavras-chave
Wing, Geometric morphometrics, Parasite vector, Principal components, Artificial neural network
Como citar
Acta Tropica. Amsterdam: Elsevier Science Bv, v. 152, p. 165-169, 2015.