Determining the Reliability of Personal Masks with Convolutional Neural Networks
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Date
2024
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Ankara Üniversitesi
Abstract
During the COVID-19 pandemic, which is a worldwide disaster, it has been proven that one of the most
important methods to struggle the transmission of such diseases is the use of face masks. Due to this
pandemic, the use of masks has become mandatory in Turkey and in many other countries. Since some
surgical masks do not comply with the standards, their protective properties are low. The aim of this study
is to determine the reliability of personal masks with Convolutional Neural Networks (CNNs). For this
purpose, first, a mask data set consisting of 2424 images was created. Subsequently, deep learning and
convolutional neural networks were employed to differentiate between meltblown surgical masks and nonmeltblown surgical masks without protective features. The masks under investigation in this study are
divided into 5 classes: fabric mask, meltblown surgical mask, meltblown surgical mask, respiratory
protective mask and valve mask. Classification of these mask images was carried out using various models,
including 4-Layer CNN, 8-Layer CNN, ResNet-50, DenseNet-121, EfficientNet-B3, VGG-16, MobileNet,
NasNetMobile, and Xception. The highest accuracy, 98%, was achieved with the Xception network.
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Keywords
Artificial Intelligence, Convolutional Neural Networks, Image classification, Personal Mask