A Computer Vision Approach to Combat Lyme Disease
- URL: http://arxiv.org/abs/2009.11931v1
- Date: Thu, 24 Sep 2020 20:00:02 GMT
- Title: A Computer Vision Approach to Combat Lyme Disease
- Authors: Sina Akbarian, Tania Cawston, Laurent Moreno, Samir Patel, Vanessa
Allen, and Elham Dolatabadi
- Abstract summary: Lyme disease is an infectious disease transmitted 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.
- Score: 0.16361045780285935
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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