Determining the Reliability of Personal Masks with Convolutional Neural Networks

Özgür Boran Ak; Ertan Kuruöz; Ayça Ak

Article Details

Determining the Reliability of Personal Masks with Convolutional Neural Networks

Authors:

Özgür Boran Ak | Ertan Kuruöz | Ayça Ak

Publisher:

Ankara University

Main Topics:

Mühendislik | Sağlık Kurumları Yönetimi

Journal:

Afet ve Risk Dergisi Vol.7 Issue 1, pp.71-85

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

2001314636

pISSN:

2636-8390

Keywords:

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 non-meltblown 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.

 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 non-meltblown 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.

 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 non-meltblown 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.

 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 non-meltblown 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.

Article Details

Determining the Reliability of Personal Masks with Convolutional Neural Networks

Authors:

Özgür Boran Ak | Ertan Kuruöz | Ayça Ak

Publisher:

Ankara University

Main Topics:

Mühendislik | Sağlık Kurumları Yönetimi

Journal:

Afet ve Risk Dergisi Vol.7 Issue 1, pp.71-85

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

2001314636

pISSN:

2636-8390

Artificial Intelligence

Convolutional Neural Networks

Image classification

Personal MaskArtificial Intelligence

Convolutional Neural Networks

Image classification

Personal Mask

Issues

Article Details

Authors: Özgür Boran Ak | Ertan Kuruöz | Ayça Ak

Publisher: Ankara University

Main Topics: Mühendislik | Sağlık Kurumları Yönetimi

Journal: Afet ve Risk Dergisi Vol.7 Issue 1, pp.71-85

Document Type: Article

Publication Date: 3/2024

Document Language: English

Document ID (Org No.): 2001314636

pISSN: 2636-8390

Issues

Authors:

Özgür Boran Ak | Ertan Kuruöz | Ayça Ak

Publisher:

Ankara University

Main Topics:

Mühendislik | Sağlık Kurumları Yönetimi

Journal:

Afet ve Risk Dergisi Vol.7 Issue 1, pp.71-85

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

2001314636

pISSN:

2636-8390