1. A Swin Transformer-based model for mosquito species identification.
- Author
-
Zhao, De-zhong, Wang, Xin-kai, Zhao, Teng, Li, Hu, Xing, Dan, Gao, He-ting, Song, Fan, Chen, Guo-hua, and Li, Chun-xiao
- Subjects
IDENTIFICATION ,DEEP learning ,CONVOLUTIONAL neural networks ,CULEX pipiens ,MOSQUITOES ,SPECIES ,MOSQUITO control ,MORPHOLOGY - Abstract
Mosquito transmit numbers of parasites and pathogens resulting in fatal diseases. Species identification is a prerequisite for effective mosquito control. Existing morphological and molecular classification methods have evitable disadvantages. Here we introduced Deep learning techniques for mosquito species identification. A balanced, high-definition mosquito dataset with 9900 original images covering 17 species was constructed. After three rounds of screening and adjustment-testing (first round among 3 convolutional neural networks and 3 Transformer models, second round among 3 Swin Transformer variants, and third round between 2 images sizes), we proposed the first Swin Transformer-based mosquito species identification model (Swin MSI) with 99.04% accuracy and 99.16% F1-score. By visualizing the identification process, the morphological keys used in Swin MSI were similar but not the same as those used by humans. Swin MSI realized 100% subspecies-level identification in Culex pipiens Complex and 96.26% accuracy for novel species categorization. It presents a promising approach for mosquito identification and mosquito borne diseases control. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF