2020/09/24 by Sina Akbarian, Tania Cawston, Akbarian, Sina +10
Computer Science · Engineering · Immunology and Microbiology · Medicine · #Artificial intelligence #Biology #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Ecology #FOS: Computer and information sciences #FOS: Electrical engineering #Identification (biology) #Image and Video Processing (eess.IV) #Immunology #Ixodes #Ixodes scapularis #Ixodidae #Lyme disease #Machine Learning (cs.LG) #Machine learning #Medicine #Mosquito-borne diseases and control #Tick #Vector-borne infectious diseases #Viral Infections and Vectors #Virology #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.11931
published in arXiv (Cornell University) (Cornell University) · Under review
arxiv created 2020/09/24 · openalex publication_date 2020/09/24 · arxiv updated 2020/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Lyme disease is an infectious disease transmitted to humans by a bite from an infected Ixodes species (blacklegged ticks). It is one of the fastest growing vector-borne illness in North America and is expanding its geographic footprint. Lyme disease treatment is time-sensitive, and can be cured by administering an antibiotic (prophylaxis) to the patient within 72 hours after a tick bite by the Ixodes species. However, the laboratory-based identification of each tick that might carry the bacteria is time-consuming and labour intensive and cannot meet the maximum turn-around-time of 72 hours for an effective treatment. Early identification of blacklegged ticks using computer vision technologies is a potential solution in promptly identifying a tick and administering prophylaxis within a crucial window period. In this work, we build an automated detection tool that can differentiate blacklegged ticks from other ticks species using advanced deep learning and computer vision approaches. We demonstrate the classification of tick species using Convolution Neural Network (CNN) models, trained end-to-end from tick images directly. Advanced knowledge transfer techniques within teacher-student learning frameworks are adopted to improve the performance of classification of tick species. Our best CNN model achieves 92% accuracy on test set. The tool can be integrated with the geography of exposure to determine the risk of Lyme disease infection and need for prophylaxis treatment.