Abstract
In response to the escalating global threat of mosquito-borne diseases, this research introduces an innovative application of deep learning techniques to address the critical need for precise mosquito identification. Utilising a diverse dataset generously contributed by citizen scientists, this study aims to utilize existing advanced computer vision models capable of accurately detecting and classifying mosquitoes. The model underwent extensive training and evaluation, demonstrating remarkable accuracy and generalization capabilities. Evaluation metrics were employed to assess the model’s performance comprehensively, including precision, recall, F1 score, accuracy, specificity and ROC AUC. The results showcase the model’s effectiveness in accurately identifying and classifying mosquitoes across various taxonomic categories and environmental conditions. By leveraging cutting-edge AI technology and engaging citizen scientists, this initiative represents a significant step forward in revolutionizing mosquito surveillance and combating the spread of mosquito-borne diseases.
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Mathoho, M., van der Haar, D., Vadapalli, H. (2024). Innovations in Mosquito Identification: Integrating Deep Learning with Citizen Science. In: Xie, X., Styles, I., Powathil, G., Ceccarelli, M. (eds) Artificial Intelligence in Healthcare. AIiH 2024. Lecture Notes in Computer Science, vol 14976. Springer, Cham. https://doi.org/10.1007/978-3-031-67285-9_14
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DOI: https://doi.org/10.1007/978-3-031-67285-9_14
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