Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection

Loey, Mohamed; Manogaran, Gunasekaran; Taha, Mohamed Hamed N.; Khalifa, Nour Eldeen M.

doi:10.1016/j.scs.2020.102600

Cited by 400 publications

(239 citation statements)

References 29 publications

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“…Although a hybrid neural network model between deep learning and machine learning model is considerably efficiency, a major problem of the proposed detector found is how to select the high quality image dataset and also the strategy for labeling an appropriate ground-truth as compatible with an objective. A recent study has been proved that a renewal dataset specific to his purpose improved the performance of the proposed detector up to 81% of average precision when comparing to a former report 46 . Overall, the deep learning approach is practical and comfortably accessible for the crowed community to facilitate the prevention effort against the mosquito-borne disease.…”

Section: Discussionmentioning

confidence: 82%

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: 82%

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

Kittichai

Pengsakul

Chumchuen

et al. 2021

Sci Rep

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“…COVID-19 is a global challenge, requiring cross-disciplinary cooperation and research to control, in order to maintain the sustainable development of society. Computational biology, big data and artificial intelligence are being used in daily monitoring, prevention and treatment of infectious diseases ( Ahmed, Ahmad, Rodrigues, Jeon, & Din, 2020 ; Bhattacharya et al, 2020 ; Loey, Manogaran, Taha, & Khalifa, 2020 ; Zhou, Qiu, Pu, Huang, & Ge, 2020 ). Abboah-Offei et al (2021) investigated the impact face mask has had in controlling transmission of respiratory viral infections.…”

Section: Discussionmentioning

confidence: 99%

Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)

Guo

Qian

Sun

et al. 2021

Sustainable Cities and Society

102

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“…( Rahman et al, 2020 ) studied the economic impacts of COVID-19 lockdown exercises using a data-driven dynamic clustering framework. Loey et al ( Loey, Manogaran, Taha, & Khalifa, 2020 ) used YOLO-v2 and ResNet-50 for facial mask detection.…”

Section: Related Workmentioning

confidence: 99%

Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic

Ahmed

Ahmad

Jeon

2021

Sustainable Cities and Society

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The recent outbreak of the COVID-19 affected millions of people worldwide, yet the rate of infected people is increasing. In order to cope with the global pandemic situation and prevent the spread of the virus, various unprecedented precaution measures are adopted by different countries. One of the crucial practices to prevent the spread of viral infection is social distancing. This paper intends to present a social distance framework based on deep learning architecture as a precautionary step that helps to maintain, monitor, manage, and reduce the physical interaction between individuals in a real-time top view environment. We used Faster-RCNN for human detection in the images. As the human's appearance significantly varies in a top perspective; therefore, the architecture is trained on the top view human data set. Moreover, taking advantage of transfer learning, a new trained layer is fused with a pre-trained architecture. After detection, the pair-wise distance between peoples is estimated in an image using Euclidean distance. The detected bounding box's information is utilized to measure the central point of an individual detected bounding box. A violation threshold is defined that uses distance to pixel information and determines whether two people violate social distance or not. Experiments are conducted using various test images; results demonstrate that the framework effectively monitors the social distance between peoples. The transfer learning technique enhances the overall performance of the framework by achieving an accuracy of 96% with a False Positive Rate of 0.6%.

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Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection

Cited by 400 publications

References 29 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

Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)

Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic

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