COVID-19 Face Mask Detection Using CNN and Transfer Learning

Cecilia Ajowho Adenusi (), Olufunke Rebecca Vincent (), Jesufunbi Abodunrin () and Bukola Taibat Adebiyi ()
Additional contact information
Cecilia Ajowho Adenusi: Linux Professional Institute, Nigeria Master Affiliate
Olufunke Rebecca Vincent: Federal University of Agriculture
Jesufunbi Abodunrin: Federal University of Agriculture
Bukola Taibat Adebiyi: Federal University of Agriculture

Chapter Chapter 22 in Decision Sciences for COVID-19, 2022, pp 393-405 from Springer

Abstract: Abstract The trauma produced by the COVID-19 sickness, which was proclaimed by the World Health Organization (WHO) in 2020, has impacted the entire world. WHO has recommended several recommendations and precautions to effectively prevent the spread of the deadly disease, including social distance, hand sanitizer, and the use of a face mask or face shield. Most particularly in crowded settings, which is what inspired this investigation into one of the WHO recommended preventive measures, namely the use of a face mask. This research used a Convolutional Neural Network and a Transfer Learning Model to determine whether or not a citizen wears a mask. This suggested model is trained and tested on the Face Masked Dataset, then image augmentation on limited available data for improved training and testing, with a 98 percent accuracy rate during training and testing.

Keywords: COVID-19; Face mask; Transfer learning; Dataset; Pandemic; Image augmentation; CNN; WHO (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-87019-5_22

Ordering information: This item can be ordered from
http://www.springer.com/9783030870195

DOI: 10.1007/978-3-030-87019-5_22

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().