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

Abstract

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.

A Systematic Literature Review on Insect Detection in Images

“…Nam et al [35] mentioned that they used BBOX [60] while other authors [10], [14], [17], [25], [28], [33], [44] used LabelImg [61] and further authors [27], [30], [50] used LabelMe [62]. Some articles [35], [42], [44] mention that the labelling is done by experts, and Rustia et al [33] state that labelling was done by experts and entomologists, while Ding et al [19] [40] did not specify which method they employed. Rotation is used by Ding et al [19], whereas reflection is used by Khalifa et al [37].…”

“…• Datasets gathered by research institutions, such as the Moth Classification and Counting (MCC) dataset and European Moths dataset (EU-Moths), which were used by Korsch et al [41], a dataset collected by the Laboratory of Medical Zoology, which was used by Luo et al [42], and a dataset collected by the Hefei Institute of Physical Science (under the name of pest24), which was used by Liu et al [43],…”

“…In the case only for classification techniques, SutHo et al [50] use MobileNetv2, Luo et al [42] use Inception-Net v3, Abeywardhana et al [16] use SqueezeNet, Porrello et al [90] use ResNet, Roosjen et al [24] use ResNet18, and Menikdiwela et al [9] utilize VGG16 fin-tuned on spiders.…”

“…-Some articles only detect one insect or one class: [9], [19], [23], [30], [41], [42], [44], [50], [52], [82], [90], -Others detect two classes: [8], [14], [26], [46], -Other papers detect 3 classes: [7], [20], [27], [28], [32], [48], [51], -Other articles detect 4 classes: [17], and [89], -Some of the studied papers detect 5 classes: [35], [10], and [39], -There are some papers that detect 6 classes: [15], [16], [18], [25], [34], -Other papers detect from 7 to 9 classes: [24] (7 classes), [31], [37], and [85] Since there are fewer studies as the number of insects increases, 66% of all articles focus on insects with a population of one to six.…”

A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model

“…In their study, mel-frequency cepstral coefficients (MFCCs) were extracted from the recordings after preprocessing, followed by classification using a trained Gaussian mixture model (GMM). With the development of deep learning and neural networks, artificial intelligence has been shown to be a promising solution for various challenges that require specialized and labor-intensive work [ 8 ]. Many researchers in recent years have held the same point of view and adopted deep learning models in their studies.…”