Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks

Couret, Jannelle; Moreira, D.C.; Bernier, Davin; Loberti, Aria Mia; Dotson, Ellen M.; Álvarez, Marco

doi:10.1371/journal.pntd.0008904

Cited by 26 publications

(27 citation statements)

References 42 publications

(55 reference statements)

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“…According to the result in this study, the state-of-art models is the greatest performance when comparing to others 9 , 35 – 38 , except for the precision reported by Minakski et al (Suppl. Table S5 ) 36 .…”

Section: Discussionmentioning

confidence: 45%

Deep learning approaches for challenging species and gender identification of mosquito vectors

Kittichai

Pengsakul

Chumchuen

et al. 2021

Sci Rep

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Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

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Section: Discussionmentioning

confidence: 45%

Deep learning approaches for challenging species and gender identification of mosquito vectors

Kittichai

Pengsakul

Chumchuen

et al. 2021

Sci Rep

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“…The “augment” pictures and their copies were then flipped and rotated such that each original image produced an additional 15 images ( Fig 3 ). This technique is called data augmentation and has been shown to effectively increase model performance by increasing the amount of data available for training [ 34 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

“…Much progress has also been made in the area of automated species identification. Deep learning has been used to identify plant species [ 31 ], mammals [ 32 ], fish [ 33 ], and insects [ 34 ] among others. However, despite the scale and impact of the tick-borne disease problem, there has been relatively little work on automated tick identification.…”

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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“…Analysis of wing beat frequency using deep learning methods has been successful in lab environments (18)(19)(20)(21), however has faced significant barriers when deployed in the field due to the high variability exhibited by field caught specimens (22). In the past few years, advances in image processing and deep learning, namely convolutional neural networks (CNNs), have shown great promise in adult mosquito species identification nearing 99% accuracy across large numbers of species (23)(24)(25)(26). The most notable works have used primarily wild caught specimens in various states of physical damage (24,25), assessed differences between cryptic species within a species complex (23), identified the sex of specimens (23), and determined when a species was novel to the CNN system, flagging it as an unknown species (25).…”

Section: Introductionmentioning

confidence: 99%

Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them

Brey¹,

Sudhakar²,

Gersch³

et al. 2022

Front. Trop. Dis

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Effective mosquito surveillance and control relies on rapid and accurate identification of mosquito vectors and confounding sympatric species. As adoption of modified mosquito (MM) control techniques has increased, the value of monitoring the success of interventions has gained recognition and has pushed the field away from traditional ‘spray and pray’ approaches. Field evaluation and monitoring of MM control techniques that target specific species require massive volumes of surveillance data involving species-level identifications. However, traditional surveillance methods remain time and labor-intensive, requiring highly trained, experienced personnel. Health districts often lack the resources needed to collect essential data, and conventional entomological species identification involves a significant learning curve to produce consistent high accuracy data. These needs led us to develop MosID: a device that allows for high-accuracy mosquito species identification to enhance capability and capacity of mosquito surveillance programs. The device features high-resolution optics and enables batch image capture and species identification of mosquito specimens using computer vision. While development is ongoing, we share an update on key metrics of the MosID system. The identification algorithm, tested internally across 16 species, achieved 98.4 ± 0.6% % macro F1-score on a dataset of known species, unknown species used in training, and species reserved for testing (species, specimens respectively: 12, 1302; 12, 603; 7, 222). Preliminary user testing showed specimens were processed with MosID at a rate ranging from 181-600 specimens per hour. We also discuss other metrics within technical scope, such as mosquito sex and fluorescence detection, that may further support MM programs.

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Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks

Cited by 26 publications

References 42 publications

Deep learning approaches for challenging species and gender identification of mosquito vectors

Deep learning approaches for challenging species and gender identification of mosquito vectors

Identification of public submitted tick images: A neural network approach

Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them

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