Evidence map›Paper›PMID 37064899›Full record

ArticleFrontiers in physiology2023

A convolutional neural network for face mask detection in IoT-based smart healthcare systems.

Bose S, Logeswari G, Thavavel Vaiyapuri, Tariq Ahamed Ahanger, Fadl Dahan, Fahima Hajjej, Ismail Keshta, Majed Alsafyani, Roobaea Alroobaea, Kaamran Raahemifar

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In one paragraph

Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Bose SDepartment of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India.
Logeswari GDepartment of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India.
Thavavel VaiyapuriDepartment of Computer Sciences, College of Computer engineering and sciences, Prince Sattam Bin AbdulAziz University, Al-Kharj, Saudi Arabia.
Tariq Ahamed AhangerDepartment of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Fadl DahanDepartment of Management Information Systems, College of Business Administration Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Fahima HajjejDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Ismail KeshtaComputer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia.
Majed AlsafyaniDepartment of Computer Science, College of Computers and Information Technology, Taif University, Al-Hawiyya, Saudi Arabia.
Roobaea AlroobaeaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Al-Hawiyya, Saudi Arabia.
Kaamran RaahemifarCollege of Information Sciences and Technology, Data Science and Artificial Intelligence Program, State College, Penn State University, State College, PA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.

Indexed as

convolutio nal neural networks (CNN)COVID-19face mask and social distancing detectionimage processingneural network

Identifiers

PMID37064899
PMCPMC10102606

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Registered trials

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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.