A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models

• Published in: Insects (Basel, Switzerland) Vol. 13; no. 2; p. 116
• Main Authors: Luo, Chu-Yuan, Pearson, Patrick, Xu, Guang, Rich, Stephen M
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.01.2022; MDPI
• Subjects: Accuracy; Algorithms; Anaplasmosis; Arachnids; Artificial intelligence; Artificial neural networks; Bacterial diseases; Biting; Classification; Climate change; Computer vision; Datasets; Deep learning; Disease transmission; Ectoparasites; Face recognition; Health risks; Human error; Identification; Identification methods; Learning algorithms; Livestock; Lyme disease; Machine learning; medical entomology; Medical imaging; Medical personnel; Morphology; Mountains; Neural networks; Object recognition; Parasitic diseases; Pattern recognition; Range extension; Rocky Mountain spotted fever; Species; Surveillance; Taxonomy; Threat evaluation; Tick-borne diseases; Ticks; Vector-borne diseases; Vectors; Viruses; Vision systems; Wild animals; United States > US; medical entomology; ticks; computer vision
• ISSN: 2075-4450; 2075-4450
• DOI: 10.3390/insects13020116

A wide range of pathogens, such as bacteria, viruses, and parasites can be transmitted by ticks and can cause diseases, such as Lyme disease, anaplasmosis, or Rocky Mountain spotted fever. Landscape and climate changes are driving the geographic range expansion of important tick species. The morphological identification of ticks is critical for the assessment of disease risk; however, this process is time-consuming, costly, and requires qualified taxonomic specialists. To address this issue, we constructed a tick identification tool that can differentiate the most encountered human-biting ticks, , , and by implementing artificial intelligence methods with deep learning algorithms. Many convolutional neural network (CNN) models (such as VGG, ResNet, or Inception) have been used for image recognition purposes but it is still a very limited application in the use of tick identification. Here, we describe the modified CNN-based models which were trained using a large-scale molecularly verified dataset to identify tick species. The best CNN model achieved a 99.5% accuracy on the test set. These results demonstrate that a computer vision system is a potential alternative tool to help in prescreening ticks for identification, an earlier diagnosis of disease risk, and, as such, could be a valuable resource for health professionals.