@Article{csse.2023.035869,
AUTHOR = {Abdullah S. AL-Malaise AL-Ghamdi, Sultanah M. Alshammari, Mahmoud Ragab},
TITLE = {Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {46},
YEAR = {2023},
NUMBER = {2},
PAGES = {1863--1877},
URL = {http://www.techscience.com/csse/v46n2/51650},
ISSN = {},
ABSTRACT = {Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks. Wearing a face mask in public areas protects people from spreading COVID-19. Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways. This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic. The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering. To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 module, showing the novelty of the work. At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model. The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset. The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.},
DOI = {10.32604/csse.2023.035869}
}