Convolutional neural network-based real-time mosquito genus identification using wingbeat frequency: A binary and multiclass classification approach

Abstract

Global rises in dengue hemorrhagic fever, especially in Asia and Latin America, underscore the necessity for enhanced public health interventions. Aedes spp. mosquitoes are the primary vectors; however, species such as Culex quinquefasciatus pose significant health risks by transmitting diseases such as filariasis, impacting millions of people worldwide. This study introduces a real-time convolutional neural network-based mosquito classification system using wingbeat frequency for identifying various mosquito species, with emphasis on Aedes sp. We proposed and assessed two models: a binary classification and a multiclass system. The binary system exhibited an outstanding accuracy of 91.76% in distinguishing between Aedes aegypti and Culex quinquefasciatus. The multiclass system accurately identified female and male Aedes aegypti and Culex quinquefasciatus with a precision of 87.16%. This innovative approach serves as a potential tool for dengue infection control and a versatile instrument for combating various mosquito-borne illnesses, enhancing vector surveillance for comprehensive disease management.