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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1999
Title: Deep learning-based mosquito species identification using convolutional neural networks
Authors: Nguyen Ha Bao Ngan 
Keywords: Deep learning;Image classification;Convolution neuron network;Mosquito identification
Issue Date: 2024
Abstract: 
This thesis explored the ability of pre-trained CNN models (VGG-16, ResNet50, Xception) to classify mosquito species based on images of their bodies and wings. The research tested these models on separate datasets of body and wing images. Xception stood out with impressive classification accuracy, achieving the highest test set scores: 95.05% for body images and 93.42% for wing images. These results suggest that Xception could be a powerful tool for accurate mosquito identification based on images. This thesis highlights the potential
of Xception for practical mosquito monitoring systems. With further development, its high accuracy could make it an asset in controlling mosquito populations and preventing diseases they transmit
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1999
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Business Information Systems (BIS)

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