A CNN-based mosquito classification using image transformation of wingbeat features

Jose Alvaro Luna-Gonzalez, Daniel Robles-Camarillo, Mariko Nakano-Miyatake, Humberto Lanz-Mendoza, Hector Perez-Meana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a classification of mosquito's specie is performed using mosquito wingbeats samples obtained by optical sensor. Six world-wide representative species of mosquitos, which are Aedes aegypti, Aedes albopictus, Anopheles arabiensis, Anopheles gambiae and Culex pipiens, Culex quinquefasciatus, are considered for classification. A total of 60, 000 samples are divided equally in each specie mentioned above. In total, 25 audio feature extraction algorithms are applied to extract 39 feature values per sample. Further, each audio feature is transformed to a color image, which shows audio features presenting by different pixel values. We used a fully connected neural networks for audio features and a convolutional neural network (CNN) for image dataset generated from audio features. The CNN-based classifier shows 90.75% accuracy, which outperforms the accuracy of 87.18% obtained by the first classifier using directly audio features.

Original languageEnglish
Title of host publicationKnowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2020
EditorsHamido Fujita, Ali Selamat, Sigeru Omatu
PublisherIOS Press BV
Pages127-137
Number of pages11
ISBN (Electronic)9781643681146
DOIs
StatePublished - 15 Sep 2020
Event19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2020 - Virtual, Online, Japan
Duration: 22 Sep 202024 Sep 2020

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume327
ISSN (Print)0922-6389

Conference

Conference19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2020
Country/TerritoryJapan
CityVirtual, Online
Period22/09/2024/09/20

Keywords

  • Audio processing
  • Classification
  • Convolutional neural networks
  • Feature extraction
  • Image-transformed information
  • Mosquitoes

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