Enhancing mosquito classification through self-supervised learning
Issued Date
2024-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85209130338
Pubmed ID
39511347
Journal Title
Scientific Reports
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.14 No.1 (2024)
Suggested Citation
Charoenpanyakul R., Kittichai V., Eiamsamang S., Sriwichai P., Pinetsuksai N., Naing K.M., Tongloy T., Boonsang S., Chuwongin S. Enhancing mosquito classification through self-supervised learning. Scientific Reports Vol.14 No.1 (2024). doi:10.1038/s41598-024-78260-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/102117
Title
Enhancing mosquito classification through self-supervised learning
Corresponding Author(s)
Other Contributor(s)
Abstract
Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model’s overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies.