A Computer Vision Approach to Combat Lyme Disease

TL;DR

This paper presents a deep learning-based computer vision tool that accurately identifies blacklegged ticks from images, enabling rapid Lyme disease risk assessment and timely prophylaxis within a critical 72-hour window.

Contribution

It introduces an automated CNN-based system with knowledge transfer techniques for precise tick species classification from images, addressing a key diagnostic bottleneck.

Findings

Achieved 92% accuracy in tick species classification

Demonstrated integration potential with geographic data for risk assessment

Enhanced classification performance using teacher-student learning frameworks

Abstract

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.