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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/2004
Title: Identification of mosquito species by utilizing transformer models on body and wing images
Authors: Tran Thi Khanh Linh 
Keywords: Image classification;Mosquito identification;Swin transformer
Issue Date: 2024
Abstract: 
This thesis investigated the effectiveness of the Swin Transformer architecture at identifying mosquito species. The two distinct datasets comprised of mosquito body and wing images were used for training and evaluating the model's effectiveness. The experiment result indicated that Swin Transformer achieved high accuracy, with the best accuracy score on test set of 97.36% for body images (variant Swin-B), and 93.42% for wing images (variant SwinS). Consequently, it unveiled the capacity of Swin Transformer to precisely classify mosquitoes based on images. Furthermore, this exploration discussed the trade-offs between accuracy levels and computational efficiency in case of limited resources. Regarding prospects, the outcomes suggested an optimistic approach, where Swin Transformer could be used in a reallife mosquito surveillance framework with some additional fine-tuning
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/2004
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Business Information Systems (BIS)

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