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

Kittichai, Veerayuth; Pengsakul, Theerakamol; Chumchuen, Kemmapon; Samung, Yudthana; Sriwichai, Patchara; Phatthamolrat, Natthaphop; Tongloy, Teerawat; Jaksukam, Komgrit; Chuwongin, Santhad; Boonsang, Siridech

doi:10.1038/s41598-021-84219-4

Cited by 50 publications

(35 citation statements)

References 39 publications

(40 reference statements)

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“…2). It was more bene cial when a combination of two deep learning models such as using two-state learning strategies of concatenated YOLO models for identifying genus, species and gender of the mosquito vector 41 . Besides, the hybrid platform of YOLOv2 with ResNet-50 detector which help improve the average precision of the proposed detector up to 81% compared to a previous single model 40 .…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the quali ed models were trained for at least 100,000 epochs in order to record the learned parameters. The likelihood of a threshold greater than and equal to 50% is considered to be a true positive value, which incur no cost 41,43 . Otherwise, result of image classi cation would produce false positive values that is unexpected in medical diagnosis.…”

Section: Data Augmentation and Model Trainingmentioning

confidence: 99%

“…The proposed methodology for classifying the blood stages of avian malaria, P. gallenaceum, using a hybrid two-stage model (object identi cation YOLOv3 and Darknet/ Densenet201 classi cation algorithms). Previously, the combination of two CNN models was reported to have increased prediction accuracy 40,41 . In this work, the rst stage of the proposed model is the YOLOv3-based object detection, which aims to distinguish a single RBC image from those inside a microscopic image.…”

Section: A Hybrid Two-stage Model: Rbc Detection and Classi Cation Modelsmentioning

confidence: 99%

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Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

Kittichai

Kaewthamasorn

Thanee

et al. 2021

Preprint

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The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of four parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with the mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.

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Section: Resultsmentioning

confidence: 99%

Section: Data Augmentation and Model Trainingmentioning

confidence: 99%

Section: A Hybrid Two-stage Model: Rbc Detection and Classi Cation Modelsmentioning

confidence: 99%

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Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

Kittichai

Kaewthamasorn

Thanee

et al. 2021

Preprint

Self Cite

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“…Moreover, Armigeres spp. (Coquillett) (Diptera: Culicidae), Anopheles spp., Musca domestica (Linnaeus) (Diptera: Muscidae), Trigona apicalis (Jurine) (Hymenoptera: Apidae), and Oryzaephilus surinamensis (Ganglbauer) (Coleoptera: Silvanidae) were also identified by this method with a 99.0% precision and 92.4% sensitivity [ 59 ].…”

Section: Advanced Approaches For Medical Parasite and Arthropod Diagnosesmentioning

confidence: 99%

State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

Ruenchit

2021

Diagnostics

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Conventional methods such as microscopy have been used to diagnose parasitic diseases and medical conditions related to arthropods for many years. Some techniques are considered gold standard methods. However, their limited sensitivity, specificity, and accuracy, and the need for costly reagents and high-skilled technicians are critical problems. New tools are therefore continually being developed to reduce pitfalls. Recently, three state-of-the-art techniques have emerged: DNA barcoding, geometric morphometrics, and artificial intelligence. Here, data related to the three approaches are reviewed. DNA barcoding involves an analysis of a barcode sequence. It was used to diagnose medical parasites and arthropods with 95.0% accuracy. However, this technique still requires costly reagents and equipment. Geometric morphometric analysis is the statistical analysis of the patterns of shape change of an anatomical structure. Its accuracy is approximately 94.0–100.0%, and unlike DNA barcoding, costly reagents and equipment are not required. Artificial intelligence technology involves the analysis of pictures using well-trained algorithms. It showed 98.8–99.0% precision. All three approaches use computer programs instead of human interpretation. They also have the potential to be high-throughput technologies since many samples can be analyzed at once. However, the limitation of using these techniques in real settings is species coverage.

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“…In recent years several artificial deep neural networks (DNNs) were introduced as a powerful tool for solving image-related problems 5 , including medical image analysis and COVID-19 pneumonia classification 6 , 7 . For instance, ResNet 8 , DenseNet 9 , CapsNet 10 , SENet 11 are some of the very successful DNNs that provide significant learning and analyze the problems similar to humans.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet

Javidi

Abbaasi

Atashi

et al. 2021

Sci Rep

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With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.

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

Cited by 50 publications

References 39 publications

Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet

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