ArticleIEEE sensors journal2021
Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone.
Article in IEEE sensors journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 65 citations in OpenAlex.
- Construction of a smart face recognition model for university libraries based on FaceNet-MMAR algorithm.PloS one · 2024Article
- An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection.Array (New York, N.Y.) · 2023Article
- A novel attention-based enhancement framework for face mask detection in complicated scenarios.Signal processing. Image communication · 2023Article
- A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk.Procedia computer science · 2023Article
- Face mask recognition system using CNN model.Neuroscience informatics · 2022Review
- A novel algorithm for mask detection and recognizing actions of human.Expert systems with applications · 2022Review
- A real time face mask detection system using convolutional neural network.Multimedia tools and applications · 2022Article
- Passenger Surveillance Using Deep Learning in Post-COVID-19 Intelligent Transportation System.Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology · 2022Article
- Deep learning techniques for detecting and recognizing face masks: A survey.Frontiers in public health · 2022Review
- Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm.Computational intelligence and neuroscience · 2022Article
- A novel machine learning scheme for face mask detection using pretrained convolutional neural network.Materials today. Proceedings · 2022Article
- Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data.Neurocomputing · 2021Article
- Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning.Healthcare (Basel, Switzerland) · 2021Article
- Automatic Quantification of Anterior Lamina Cribrosa Structures in Optical Coherence Tomography Using a Two-Stage CNN Framework.Sensors (Basel, Switzerland) · 2021Article
- Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review.Soft computing · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we do not know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the gray level co-occurrence matrixes (GLCMs) of the face mask's micro-photos. Next, a three-result detection system is accomplished by using K Nearest Neighbor (KNN) algorithm. The results of validation experiments show that our system can reach an accuracy of 82.87% (measured by macro-measures) on the testing dataset. The precision of Type I 'normal use' and the recall of type III 'not recommended' reach 92.00% and 92.59%. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.