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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
DC FieldValueLanguage
dc.contributor.authorNguyen Ha Bao Nganen_US
dc.date.accessioned2025-03-26T06:44:57Z-
dc.date.available2025-03-26T06:44:57Z-
dc.date.issued2024-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1999-
dc.description.abstractThis 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 transmiten_US
dc.language.isovien_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDeep learningen_US
dc.subjectImage classificationen_US
dc.subjectConvolution neuron networken_US
dc.subjectMosquito identificationen_US
dc.titleDeep learning-based mosquito species identification using convolutional neural networksen_US
dc.typeThesisen_US
item.fulltextWith Fulltext-
item.languageiso639-1other-
item.grantfulltextrestricted-
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