Using convolutional neural networks for tick image recognition – a preliminary exploration

Omodior, Oghenekaro; Saeedpour-Parizi, Mohammad R.; Rahman, Md. Khaledur; Azad, Ariful; Clay, Keith

doi:10.1007/s10493-021-00639-x

Cited by 8 publications

(11 citation statements)

References 44 publications

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“…Thus, computer vision and deep learning algorithms could be a powerful tool to replace the current tick identification methods. To date, there have been two studies that have applied computer vision in tick species identification [22,23]. The first study focused on Ixodes scapularis (the blacklegged tick) [22] while the second study compared slightly less than 2000 images from four tick species that were captured in the state of Indiana, USA [23].…”

Section: Introductionmentioning

confidence: 99%

“…To date, there have been two studies that have applied computer vision in tick species identification [22,23]. The first study focused on Ixodes scapularis (the blacklegged tick) [22] while the second study compared slightly less than 2000 images from four tick species that were captured in the state of Indiana, USA [23]. To improve on this, we sought to develop computer vision algorithms to discern the three major human-biting ticks of North America: Amblyomma americanum (lone star tick), Dermacentor variabilis (American dog tick), and Ixodes scapularis.…”

Section: Introductionmentioning

confidence: 99%

“…To train a deep learning model, we used a novel large-scale tick dataset, which consists of 12,000 high-resolution micrographs collected from a passive surveillance system. In this dataset, all tick species were molecularly confirmed by a species-specific TaqMan PCR assay, which prevents the human error that may occur in visually identified methods [22,23]. With the ground truth label, this large tick image dataset was applied to several well-known CNN models, such as VGG [25], ResNet [26], Inception [27], MobileNet [28], and DenseNet [29].…”

Section: Introductionmentioning

confidence: 99%

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

Luo

Pearson

et al. 2022

Insects

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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, Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis, 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Luo

Pearson

et al. 2022

Insects

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“…Their classifier achieved a best accuracy of 92%, but is limited in its predictive power by only identifying a single tick species. Omodior et al [ 36 ] trained a neural network on images captured using a microscope to distinguish between A . americanum , D .…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, we are the first to use a user-generated dataset for training where ticks are photographed amidst chaotic, non-standard environments and backgrounds. We used image augmentation to increase the size of our dataset to more than 90,000 images; a dataset much larger than the datasets used in other reports on deep-learning-based tick identification [ 35 , 36 ]. We systematically compared and tuned several convolutional neural network architectures and report our optimal hyperparameter configuration which uses InceptionV3 architecture and ImageNet weights.…”

Section: Introductionmentioning

confidence: 99%

Identification of public submitted tick images: A neural network approach

et al. 2021

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Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call “TickIDNet,” that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network’s ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet’s performance can be increased by using confidence thresholds to introduce an “unsure” class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.

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Deep learning in disease vector image identification

Bai,

Shi,

Yang

2024

Pest Management Science

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Vector‐borne diseases (VBDs) represent a critical global public health concern, with approximately 80% of the world's population at risk of one or more VBD. Manual disease vector identification is time‐consuming and expert‐dependent, hindering disease control efforts. Deep learning (DL), widely used in image, text, and audio tasks, offers automation potential for disease vector identification. This paper explores the substantial potential of combining DL with disease vector identification. Our aim is to comprehensively summarize the current status of DL in disease vector identification, covering data collection, data preprocessing, model construction, evaluation methods, and applications in identification spanning from species classification to object detection and breeding site identification. We also discuss the challenges and possible prospects for DL in disease vector identification for further research. © 2024 Society of Chemical Industry.

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Using convolutional neural networks for tick image recognition – a preliminary exploration

Cited by 8 publications

References 44 publications

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

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

Identification of public submitted tick images: A neural network approach

Deep learning in disease vector image identification

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