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A Transfer Learning Approach to Detect Face Mask in COVID-19 Pandemic
University of Chittagong, Chittagong, Bangladesh.
University of Chittagong, Chittagong, Bangladesh.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2023 (English)In: Intelligent Computing & Optimization: Proceedings of the 5th International Conference on Intelligent Computing and Optimization 2022 (ICO2022) / [ed] Pandian Vasant; Gerhard-Wilhelm Weber; José Antonio Marmolejo-Saucedo; Elias Munapo; J. Joshua Thomas, Springer, 2023, 1, p. 948-957Chapter in book (Refereed)
Abstract [en]

COVID-19 is tumultuous creating our life so unpredictable. There has no solution of this contagious disease rather than vaccination and prevention. The first and foremost preventative step is using face masks. Face mask can hindrance its droplet from one to another. So this paper has focused the detection of facial mask from image processing using Transfer Learning. For this purpose, total 1376 images have been collected where 690 images of with mask and 686 images of without a mask. Here transfer learning is chosen for the reason of its capability to produce best accurate regardless the limited size of the image dataset. Here, multifarious transfer learning models have been trained to find out the best fitting model. Finally, We have found the VGG16 model with the best accuracy where training accuracy is 98.25% and testing accuracy is 96.38%.

Place, publisher, year, edition, pages
Springer, 2023, 1. p. 948-957
Series
Lecture Notes in Networks and Systems (LNNS), ISSN 2367-3370, E-ISSN 2367-3389 ; 569
Keywords [en]
Face mask, Transfer learning, VGG16, Pooling layer, Fully connected layer, K-fold cross-validation, ResNet50, ResNet101, EfficientNetB7, NASNet
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-94207DOI: 10.1007/978-3-031-19958-5_89Scopus ID: 2-s2.0-85144550722OAI: oai:DiVA.org:ltu-94207DiVA, id: diva2:1712533
Note

ISBN för värdpublikation: 978-3-031-19958-5; 978-3-031-19957-8 

Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2024-03-07Bibliographically approved

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Andersson, Karl

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