Background: Lyme disease (caused by Borrelia burgdorferi) is an infectious disease transmitted to humans by a bite from infected blacklegged ticks (Ixodes scapularis) in eastern North America. Lyme disease can be prevented if antibiotic prophylaxis is given to a patient within 72 hours of a blacklegged tick bite. Therefore, recognizing a blacklegged tick could facilitate the management of Lyme disease. Methods: In this work, we build an automated detection tool that can differentiate blacklegged ticks from other tick species using advanced computer vision approaches in real-time. Specially, we use convolution neural network models, trained end-to-end, to classify tick species. Also, advanced knowledge transfer techniques are adopted to improve the performance of convolution neural network models. Results: Our best convolution neural network model achieves 92% accuracy on unseen tick species. Conclusion: Our proposed vision-based approach simplifies tick identification and contributes to the emerging work on public health surveillance of ticks and tick-borne diseases. In addition, it can be integrated with the geography of exposure and potentially be leveraged to inform the risk of Lyme disease infection. This is the first report of using deep learning technologies to classify ticks, providing the basis for automation of tick surveillance, and advancing tick-borne disease ecology and risk management.

中文翻译：

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电动汽车的纵向运动控制：使用无源性的全球局部模型和设计


背景：莱姆病（由伯氏疏螺旋体引起）是一种传染病，通过北美洲东部受感染的黑腿蜱（肩胛硬蜱）叮咬传播给人类。如果患者在被黑腿蜱叮咬后 72 小时内给予抗生素预防，则可以预防莱姆病。因此，识别黑腿蜱可以促进莱姆病的治疗。方法：在这项工作中，我们构建了一种自动检测工具，可以使用先进的计算机视觉方法实时区分黑腿蜱与其他蜱种。特别地，我们使用经过端到端训练的卷积神经网络模型来对蜱种进行分类。此外，采用先进的知识转移技术来提高卷积神经网络模型的性能。结果：我们最好的卷积神经网络模型对看不见的蜱虫种类的准确率达到 92%。结论：我们提出的基于视觉的方法简化了蜱虫识别，并有助于蜱虫和蜱传疾病的公共卫生监测的新兴工作。此外，它可以与暴露地理相结合，并有可能被用来告知莱姆病感染的风险。这是第一份利用深度学习技术对蜱虫进行分类的报告，为蜱虫监测的自动化提供基础，并推进蜱传疾病生态学和风险管理。