Detection of Aedes aegypti larvae using single shot multibox detector with transfer learning

Fuad, Mohamad Aqil Mohd; Ghani, Mohd Ruddin Ab; Ghazali, Rozaimi; Izzuddin, Tarmizi Ahmad; Sulaima, Mohamad Fani; Jano, Zanariah; Sutikno, Tole

doi:10.11591/eei.v8i2.1263

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…However, the data acquisition steps of these previous works involved considerable manual feature extraction and large quantities of data/images, making the approaches laborious and time consuming and unable to be performed in real time. Deep convolutional neural networks (DCNNs) of deep learning (DL) are state-of-the-art methods for object recognition and classification, including for agricultural pests and mosquito larva 8 , 9 . With feature extraction in the neural network layers, DL has a high potential to make the development of a model easier and more accurate 10 .…”

Section: Introductionmentioning

confidence: 99%

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Ong

Nair

Isawasan

et al. 2021

Sci Rep

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Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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Section: Introductionmentioning

confidence: 99%

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Ong

Nair

Isawasan

et al. 2021

Sci Rep

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show abstract

“…IoT-based sensors and healthcare systems have made the dengue-combating system more resilient, which not only detects infected patients but also, on admittance to a hospital, helps in disease management [38,39]. If included in IoT systems [40,41], artificial intelligence can easily handle the data generated by the sensors, which need the supervision of medical personnel for understanding. Moreover, these techniques effectively detect any anomaly, enhancing the security aspect of IoT-based healthcare systems and making them intelligent [42,43].…”

Section: Cloud Computing and Iot-based Dengue Healthcarementioning

confidence: 99%

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

et al. 2022

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Dengue fever has earned the title of a rapidly growing global epidemic since the disease-causing mosquito has adapted to colder countries, breaking the notion of dengue being a tropical/subtropical disease only. This infectious time bomb demands timely and proper treatment as it affects vital body functions, often resulting in multiple organ failures once thrombocytopenia and internal bleeding manifest in the patients, adding to morbidity and mortality. In this paper, a tool is used for data collection and analysis for predicting dengue infection presence and estimating risk levels to identify which group of dengue infections the patient suffers from, using a machine-learning-based tertiary classification technique. Based on symptomatic and clinical investigations, the system performs real-time diagnosis. It uses warning indicators to alert the patient of possible internal hemorrhage, warning them to seek medical assistance in case of this disease-related emergency. The proposed model predicts infection levels in a patient based on the classification provided by the World Health Organization, i.e., dengue fever, dengue hemorrhagic fever, and dengue shock syndrome, acquiring considerably high accuracy of over 90% along with high sensitivity and specificity values. The experimental evaluation of the proposed model acknowledges performance efficiency and utilization through statistical approaches.

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Review of machine learning techniques for mosquito control in urban environments

Joshi

Miller

2021

Ecological Informatics

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Detection of Aedes aegypti larvae using single shot multibox detector with transfer learning

Cited by 7 publications

References 13 publications

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

Review of machine learning techniques for mosquito control in urban environments

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