Enhancing mosquito classification through self-supervised learning

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dc.contributor.authorCharoenpanyakul, Ratana
dc.contributor.authorKittichai, Veerayuth
dc.contributor.authorEiamsamang, Songpol
dc.contributor.authorSriwichai, Patchara
dc.contributor.authorPinetsuksai, Natchapon
dc.contributor.authorNaing, Kaung Myat
dc.contributor.authorTongloy, Teerawat
dc.contributor.authorBoonsang, Siridech
dc.contributor.authorChuwongin, Santhad
dc.date.accessioned2026-08-06T10:47:36Z
dc.date.available2026-08-06T10:47:36Z
dc.date.issued2024-12-01
dc.description.abstractTraditional 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.
dc.identifier.citationScientific Reports, 14(1), 2024
dc.identifier.doi10.1038/s41598-024-78260-2
dc.identifier.issn20452322
dc.identifier.other2-s2.0-85209130338
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16033
dc.sourceScientific Reports
dc.subjectMobile phone application
dc.subjectMosquito vectors
dc.subjectSelf-distillation with unlabeled data
dc.subjectSelf-supervised learning
dc.titleEnhancing mosquito classification through self-supervised learning
dc.typeArticle

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