A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models
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 morphol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Insects |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4450/13/2/116 |
| _version_ | 1827654450678857728 |
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| author | Chu-Yuan Luo Patrick Pearson Guang Xu Stephen M. Rich |
| author_facet | Chu-Yuan Luo Patrick Pearson Guang Xu Stephen M. Rich |
| author_sort | Chu-Yuan Luo |
| collection | DOAJ |
| description | 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, <i>Amblyomma americanum</i>, <i>Dermacentor variabilis</i>, and <i>Ixodes scapularis,</i> 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. |
| first_indexed | 2024-03-09T21:43:17Z |
| format | Article |
| id | doaj.art-9e20dc143b9049f0bb8b8295f2c06479 |
| institution | Directory Open Access Journal |
| issn | 2075-4450 |
| language | English |
| last_indexed | 2024-03-09T21:43:17Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Insects |
| spelling | doaj.art-9e20dc143b9049f0bb8b8295f2c064792023-11-23T20:26:27ZengMDPI AGInsects2075-44502022-01-0113211610.3390/insects13020116A Computer Vision-Based Approach for Tick Identification Using Deep Learning ModelsChu-Yuan Luo0Patrick Pearson1Guang Xu2Stephen M. Rich3Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USADepartment of Microbiology, University of Massachusetts, Amherst, MA 01003, USADepartment of Microbiology, University of Massachusetts, Amherst, MA 01003, USADepartment of Microbiology, University of Massachusetts, Amherst, MA 01003, USAA 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, <i>Amblyomma americanum</i>, <i>Dermacentor variabilis</i>, and <i>Ixodes scapularis,</i> 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.https://www.mdpi.com/2075-4450/13/2/116medical entomologytickscomputer vision |
| spellingShingle | Chu-Yuan Luo Patrick Pearson Guang Xu Stephen M. Rich A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models Insects medical entomology ticks computer vision |
| title | A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models |
| title_full | A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models |
| title_fullStr | A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models |
| title_full_unstemmed | A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models |
| title_short | A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models |
| title_sort | computer vision based approach for tick identification using deep learning models |
| topic | medical entomology ticks computer vision |
| url | https://www.mdpi.com/2075-4450/13/2/116 |
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