Automated identification of spotted-fever tick vectors using convolutional neural networks.

ABSTRACT: Ticks are key ectoparasites for the One Health approach, as they are vectors of pathogens that infect humans, domestic and wild animals. The bacteria Rickettsia rickettsii and R. parkeri are the aetiological agents of tick-borne spotted fever (SF) in South America, where Amblyomma sculptum, A. aureolatum, A. ovale and A. triste are the main vectors. Studies in the medical and biological fields show that artificial intelligence, through machine learning, has great potential to assist researchers and health professionals in image identification practices. The aim of this study was to evaluate the performance of the Convolutional Neural Networks (CNN) AlexNet, ResNet-50 and MobileNetV2 for identifying tick species transmitting SF bioagents. We organised an image database with the following gr

SUBMITTER: Gomes IRC 

PROVIDER: S-EPMC12586270 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature