Authors
Fu-Hsing Wu, Chuen-Horng Lin, Xin-Yi Zhang, Ching-Tung Peng, Kuo-Cheng Tseng, Chuan-Kang Liu, Shao-Wei Chu, Yu-Yi Chen, Wu-Chun Tu, Yung-Kuan Chan
Published in
Journal of computational biology : a journal of computational molecular cell biology. Pages 15578666261463268. Jul 06, 2026. Epub Jul 06, 2026.
Abstract
Vector mosquito bites can significantly impact quality of life, pose health risks, and even lead to death. Different mosquito species can transmit various diseases, and their blood-sucking behavior varies by sex. Therefore, accurately identifying mosquito species and gender in a given area is crucial for epidemic prevention. This study aimed to develop an image processing and artificial intelligence (AI) system to accurately identify mosquito species and gender using mosquito images. An image dataset consisting of 12,552, 17,152, and 9853 images, captured against white, yellow sticky, and blue sticky paper backgrounds, respectively, and covering eight mosquito species, was employed to develop an advanced identification system. This system integrates image processing methods, YOLO-V3 models for segmenting individual mosquito images, and Inception-V4 models for identifying mosquito species and determining their gender. The proposed models achieved impressive accuracy rates of 0.9806, 0.9888, and 0.9899 for species identification, and 0.8741, 0.9173, and 0.9241 for gender identification, corresponding to the white, yellow sticky, and blue sticky paper backgrounds, respectively. Overall, our system demonstrates a high level of accuracy in identifying both mosquito species and gender.
PMID:
42405590
Bibliographic data and abstract were imported from PubMed on 06 Jul 2026.
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