A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

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

doi:10.1016/j.measurement.2020.108288

Cited by 645 publications

(393 citation statements)

References 32 publications

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“…The accuracy results for the social Internet of Things (IoT) prediction model were from 80 to 90%. The hybrid proposed model was established in [ 27 ] using deep learning and classical machine learning for mask detection. SVM, DTs, and other collections of machine learning algorithms were selected for the investigation.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

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Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

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“…The RetinaMask also took the context information into account, and try to extract the robust feature. The work of [19] presented a hybrid detection framework which combined the deep neural network and conventional machine learning algorithms. [20] implement the medical face mask detection by combining the ResNet-50 and the classical single-stage generic detector clean face hand-masked face non-hand-masked faces masked incorrect face masked correct face FIGURE 2: Examples of our dataset.…”

Section: A Face Detectionmentioning

confidence: 99%

A Novel Detection Framework About Conditions of Wearing Face Mask for Helping Control the Spread of COVID-19

et al. 2021

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Properly wearing a face mask has become an effective way to limit the COVID-19 transmission. In this work, we target at detecting the fine-grained wearing state of face mask: face without mask, face with wrong mask, face with correct mask. This task has two main challenging points: 1) absence of practical datasets, and 2) small intra-class distance and large inter-class distance. For the first challenging point, we introduce a new practical dataset covering various conditions, which contains 8635 faces with different wearing status. For the second challenging point, we propose a novel detection framework about conditions of wearing face mask, named Context-Attention R-CNN, which enlarge the intra-class distance and shorten inter-class distance by extracting distinguishing features. Specifically, we first extract the multiple context feature for region proposals, and use attention module to weight these context feature from channel and spatial levels. And then, we decoupling the classification and localization branches to extract more appropriate feature for these two tasks respectively. Experiments show that the Context-Attention R-CNN achieves 84.1% mAP on our proposed dataset, outperforming Faster R-CNN by 6.8 points. Moreover, Context-Attention R-CNN still exceed some state-of-the-art single-stage detectors.

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“…For instance, Loey et al . [ 31 ] used a deep learning model and conventional machine learning methods for automated face mask detection. They deployed support vector machines, decision trees and ensemble method for the classification task.…”

Section: Related Workmentioning

confidence: 99%

Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

et al. 2021

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Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive tools. However, limited work has been performed towards the modelling of complex associations between the combined demographic attributes and varying nature of the COVID-19 infections across the globe. This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations. We gather multiple demographic attributes and COVID-19 infection data (by 8 January 2021) from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data. Statistical results and experts' reports indicate strong associations between COVID-19 severity levels across the globe and certain demographic attributes, e.g. female smokers, when combined together with other attributes. The outcomes will aid the understanding of the dynamics of disease spread and its progression, which in turn may support policy makers, medical specialists and society, in better understanding and effective management of the disease.

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A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

Cited by 645 publications

References 32 publications

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

A Novel Detection Framework About Conditions of Wearing Face Mask for Helping Control the Spread of COVID-19

Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

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