A framework based on deep neural networks to extract anatomy of mosquitoes from images

Minakshi, Mona; Bharti, Pratool; Bhuiyan, Tanvir; Kariev, Sherzod; Chellappan, Sriram

doi:10.1038/s41598-020-69964-2

Cited by 20 publications

(19 citation statements)

References 20 publications

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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 . The proposed model exhibited an excellent performance with high precision and recall/sensitivity levels of more than 92% in identifying the genus and species of mosquito vectors considering the morphological characteristics of the mosquitoes.…”

Section: Discussionmentioning

confidence: 99%

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: 99%

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

Kittichai

Pengsakul

Chumchuen

et al. 2021

Sci Rep

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“…Minakshi et al [31] classified images of nine different mosquito species collected in-the-wild using a multi-class support vector machine. This study was then extended by developing a mask R-CNN model to extract and detect the key anatomical components of different mosquito species, such as the thorax, wings, abdomen, and legs [30].…”

Section: Related Workmentioning

confidence: 99%

On the Use of Uncertainty in Classifying Aedes Albopictus Mosquitoes

Adhane

Dehshibi

Masip

2022

IEEE J. Sel. Top. Signal Process.

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The re-emergence of mosquito-borne diseases (MBDs), which kill hundreds of thousands of people each year, has been attributed to increased human population, migration, and environmental changes. Convolutional neural networks (CNNs) have been used by several studies to recognise mosquitoes in images provided by projects such as Mosquito Alert to assist entomologists in identifying, monitoring, and managing MBD. Nonetheless, utilising CNNs to automatically label input samples could involve incorrect predictions, which may mislead future epidemiological studies. Furthermore, CNNs require large numbers of manually annotated data. In order to address the mentioned issues, this paper proposes using the Monte Carlo Dropout method to estimate the uncertainty scores in order to rank the classified samples to reduce the need for human supervision in recognising Aedes albopictus mosquitoes. The estimated uncertainty was also used in an active learning framework, where just a portion of the data from large training sets was manually labelled. The experimental results show that the proposed classification method with rejection outperforms the competing methods by improving overall performance and reducing entomologist annotation workload. We also provide explainable visualisations of the different regions that contribute to a set of samples' uncertainty assessment.

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“…Machine learning has already been successfully utilised within entomology for a number of species classification tasks, such as the identification of pest insect species [30], the recognition of lepidopteran species [31], and the classification of mosquito species [32][33][34][35]. Additionally, automatic tools have been developed to count the eggs laid by female mosquitos, which can be used to estimate fecundity [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries

Fowler

Lees

Fagbohoun³

et al. 2021

Insects

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Pyriproxyfen (PPF) may become an alternative insecticide for areas where pyrethroid-resistant vectors are prevalent. The efficacy of PPF can be assessed through the dissection and assessment of vector ovaries. However, this reliance on expertise is subject to limitations. We show here that these limitations can be overcome using a convolutional neural network (CNN) to automate the classification of egg development and thus fertility status. Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l. An. gambiae Akron, and An. funestus s.l., whose fertility status and PPF exposure were known. Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy. This CNN model achieved an accuracy score of 94%, and application took a mean time of 38.5 s. Such a CNN can achieve an acceptable level of precision in a quick, robust format and can be distributed in a practical, accessible, and free manner. Furthermore, this approach is useful for measuring the efficacy and durability of PPF treated bednets, and it is applicable to any PPF-treated tool or similarly acting insecticide.

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A framework based on deep neural networks to extract anatomy of mosquitoes from images

Cited by 20 publications

References 20 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

On the Use of Uncertainty in Classifying Aedes Albopictus Mosquitoes

The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries

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