Mosquito Species Identification Using Convolutional Neural Networks With
www.nature.com/scientificreports OPEN Mosquito species identifcation using convolutional neural networks with a multitiered ensemble model for novel species detection Adam Goodwin1,2*, Sanket Padmanabhan1,2, Sanchit Hira2,3, Margaret Glancey1,2, Monet Slinowsky2, Rakhil Immidisetti2,3, Laura Scavo2, Jewell Brey2, Bala Murali Manoghar Sai Sudhakar1, Tristan Ford1,2, Collyn Heier2, Yvonne‑Marie Linton4,5,6, David B. Pecor4,5,6, Laura Caicedo‑Quiroga4,5,6 & Soumyadipta Acharya2* With over 3500 mosquito species described, accurate species identifcation of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open‑set problem with a detection algorithm for novel species. Closed‑set classifcation of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed‑set classifcation of 39 species produces a macro F1‑score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild‑caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region. Mosquitoes are one of the deadliest animals in the world, infecting between 250–500 million people every year with a wide range of fatal or debilitating diseases, including malaria, dengue, chikungunya, Zika and West Nile Virus1.
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