Aedes mosquito detection in its larval stage using deep neural networks

Arista-Jalife, Antonio; Nakano-Miyatake, Mariko; Garcia-Nonoal, Zaira; Robles-Camarillo, Daniel; Pérez-Meana, Héctor; Arista-Viveros, H. A.

doi:10.1016/j.knosys.2019.07.012

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…The need for community participation in blood-sucking invasive species identification, for example, Aedes (Ochloretatus) albopictus, Skuse 1895 has pushed deep learning methodology in the entomological survey field. An increasing number of studies are published, focusing solely on this invasive species with identification challenges on imago [33][34][35][36] or larval stage 37 . In addition, the design of traps with embedded systems for counting trapped insects opens up possibilities for real-time surveillance of insect density, a crucial parameter in the survey of insect vectors of medical or veterinary interest 38 .…”

Section: Discussionmentioning

confidence: 99%

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

Cannet

Chane

Akhoundi

et al. 2022

Sci Rep

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A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven’t been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy.

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Section: Discussionmentioning

confidence: 99%

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

Cannet

Chane

Akhoundi

et al. 2022

Sci Rep

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“…albopictus eggs using SEM and micro-CT yielded visible attributes to differentiate these species' eggs for identification. However, these methods are time-consuming due to the transportation problems when the samples are far from the central laboratory 20 , stressfulness for the experts who manually identify each sample 21 , and complicated procedures. With consideration of the short life span of mosquitoes, these conventional methods make vector control more difficult.…”

Section: Identification Of Aedes Aegypti and Aedes Albopictus Eggs Ba...mentioning

confidence: 99%

“…Although several attempts based on technology solutions have been made to improve the efficiency in vector surveillance [20][21][22][23][24] , the earliest stage that can be done for automatic identification is at the larval stage. Current attempts on mosquito egg identification [25][26][27][28] still focus on counting the number of Ae.…”

Section: Identification Of Aedes Aegypti and Aedes Albopictus Eggs Ba...mentioning

confidence: 99%

Identification of Aedes aegypti and Aedes albopictus eggs based on image processing and elliptic fourier analysis

Gunara,

Joelianto,

Ahmad

2023

Sci Rep

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Dengue hemorrhagic fever is a worldwide epidemic caused by dengue virus and spread by infected female mosquitoes. The two main mosquito species vectors of the dengue virus are Aedes aegypti and Aedes albopictus. Conventionally, the identification of these two species’ egg is time-consuming which makes vector control more difficult. However, although attempts on efficiency improvements by providing automatic identification have been conducted, the earliest stage is at the larval stage. In addition, there are currently no studies on classifying to distinguish the two vectors during the egg stage based on their digital image. A total of 140 egg images of Aedes aegypti and Aedes albopictus were collected and validated by rearing them individually to become adult mosquitoes. Image processing and elliptic Fourier analysis were carried out to extract and describe the shape difference of the two vectors’ eggs. Machine learning algorithms were then used to classify the shape signatures. Morphometrically, the two species’ eggs were significantly different, which Aedes albopictus were smaller in size. Egg-shape contour reconstructions of principal components and Multivariate Analysis of Variance (MANOVA) revealed that there is a significant difference (p value $$< 0.000$$ < 0.000 ) in shape between two species’ eggs at the posterior end. Based on Wilk’s lambda of the MANOVA results, the classification could be done using only the first 3 principal components. Classification of the test data yielded an accuracy of 85.00% and F1 score 84.21% with Linear Discriminant Analysis applying default hyperparameter. Alternatively, k-Nearest Neighbors with optimal hyperparameter yielded a higher classification result with 87.50% and 87.18% of accuracy and F1 score, respectively. These results demonstrate that the proposed method can be used to classify Aedes aegypti and Aedes albopictus eggs based on their digital image. This method provides a foundation for improving the identification and surveillance of the two vectors and decision making in developing vector control strategies.

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“…Siddiqua et al [ 21 ] used Inception V2 and faster R-CNN to detect dengue. To detect Aedes aegypti Linnaeus, 1762 and Aedes albopictus , a mosquito classification and detection technique was developed using AlexNet and a support vector machine (SVM), and the features of each body part were extracted [ 22 , 23 ]. Despite the attempts to develop various mosquito detection and classification models, most of them involved image classification rather than object detection, or the results for similar species were not specified.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea

Lee

Kim

Cho

2023

Insects

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Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.

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Aedes mosquito detection in its larval stage using deep neural networks

Cited by 17 publications

References 22 publications

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

Identification of Aedes aegypti and Aedes albopictus eggs based on image processing and elliptic fourier analysis

Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea

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