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Determining the Reliability of Personal Masks with Convolutional Neural Networks

Year 2024, Volume: 7 Issue: 1, 71 - 85, 29.03.2024

Özgür Boran Ak , Ertan Kuruöz , Ayça Ak

https://doi.org/10.35341/afet.1284220

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.

Keywords

Artificial Intelligence Convolutional Neural Networks Image classification Personal Mask

References

  • Almghraby, M. and Elnady, AO. (2021). Face Mask Detection in Real-Time using MobileNetv2. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958 (Online), Volume-10 Issue-6, pp: 104-108.
  • Asif, S., Wenhui, Y., Tao, Y., Jinhai, S. and Amjad, K. (2021). Real Time Face Mask Detection System using Transfer Learning with Machine Learning Method in the Era of Covid-19 Pandemic. 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), 70-75. Doi: 10.1109/ICAIBD51990.2021.9459008.
  • Bozkurt, F. (2022). A Comparative Study on Classifying Human Activities Using Classical Machine and Deep Learning Methods. Arabian Journal for Science and Engineering 47:1507–1521. Doi: doi.org/10.1007/s13369-021-06008-5
  • Cahvda, A., Dsouza, J., Badgujar S. and Damani A. (2021). Multi-Stage CNN Architecture for Face Mask Detection. 6th International Conference for Convergence in Technology (I2CT) Pune, India. Doi: 10.1109/I2CT51068.2021.9418207.
  • Chen, B., Ju, X., Xiao, B. et al. (2021). Locally GAN-generated face detection based on an improved Xception. Information Sciences Volume 572, September 2021, Pages 16-28.
  • Ciuffreda, S., Picotti, C., Pescio, P. (2021). Medical face masks on the market: Review of materials, characteristics and performed tests. Medical Device Testing. Eurofins Biolab.
  • Daşgın, A., Adem, K. & Kılıçarslan, S. (2023). Detection of Face Mask with Convolutional Neural Network Models to Reduce Covid19 Spread. Journal of the Institute of Science and Technology, 13(3): 1511-1527.
  • Du, X., Cai, Y., Wang, S. and Zhang, L. (2016). Overview of deep learning. 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). 159-164, Doi: 10.1109/YAC.2016.7804882. Fırat, H., Hanbay, D. (2023). Comparison of 3D CNN based deep learning architectures using hyperspectral images, Journal of the Faculty of Engineering and Architecture of Gazi University, 38:1, 521-534.
  • Goyal, H., Sidana, K., Singh, C., et al. (2022). A real time face mask detection system using convolutional neural network. Multimed Tools Applications. 81, 14999–15015 Doi: 10.1007/s11042-022-12166-x.
  • Hariri, W. (2022). Efficient masked face recognition method during the COVID-19 pandemic. SIViP 16, 605–612. https://doi.org/10.1007/s11760-021-02050-w.
  • Hasan, N., Bao, Y., Shawon, A. et al. (2021). DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image. SN COMPUT. SCI. 2, 389. https://doi.org/10.1007/s42979-021-00782-7.
  • Kansal, I., Popli, R. and Singla C. (2021). Comparative Analysis of various Machine and Deep Learning Models for Face Mask Detection using Digital Images. 9th International Conference on Reliability, Infocom Technologies and Optimization. Doi: 10.1109/ICRITO51393.2021.9596407.
  • Kayalı, D., Dimililer, K., Sekeroğlu, B. (2021). Face Mask Detection and Classification for COVID-19 using Deep Learning. International Conference on INnovations in Intelligent SysTems and Applications (INISTA). 10.1109/INISTA52262.2021.9548642.
  • Kaur, G., Sinha, R., Tiwari, PK., Yadav, SK., Pandey, P., Raj, R., Vashisth, A., Rakhra, M. (2022). Face mask recognition system using CNN model. Neuroscience Informatics, 2, 3, 100035. Doi: /10.1016/j.neuri.2021.100035.
  • Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, DJ. (2021). 1D convolutional neural networks and applications: A survey, Mechanical Systems and Signal Processing. 151, 107398. Doi: 10.1016/j.ymssp.2020.107398.
  • Köklü, M., Çınar, I., Taşpınar, YS. (2022). CNN-based bi-directional and directional long-short term memory network for determination of face mask. Biomedical Signal Processing and Control. 71. Doi: 10.1016/j.bspc.2021.103216.
  • Naufal, MF. et al. (2021). Comparative Analysis of Image Classification Algorithms for Face Mask Detection. Journal of Information Systems Engineering and Business Intelligence. Doi: 10.20473/jisebi.7.1.56-66.
  • O’Kelly, E., Arora, A., Pirog, S., Ward, J., Clarkson, PJ. (2021). Comparing the fit of N95, KN95, surgical, and cloth face masks and assessing the accuracy of fit checking. Plos One. 16(1): e0245688. Doi:10.1371/journal.pone.0245688.
  • O’Shea, K., Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv preprint arXiv: 1511.08458.
  • Rajath, AN., Shruthi, BM., Ambareen, K. and Lakshmi, CMS. (2021). An Adaptive Approach to Detect Face Mask in Real Time using Convolutional Neural Network (CNN) Model, 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques, 826-831. Doi: 10.1109/ICEECCOT52851.2021.9708046.
  • Rokhana, R., Herulambang, W., Indraswari, R. (2021). Multi-Class Image Classification Based on MobileNetV2 for Detecting the Proper Use of Face Mask. International Electronics Symposium. Doi: 10.1109/IES53407.2021.9594022.
  • Sighencea, BI., Stanciu, RI., C˘aleanu, CD. (2021). A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction. Sensors, 21, 7543. Doi: 10.3390/s21227543.
  • Snyder, S. and Husari, G. (2021). Thor: A Deep Learning Approach for Face Mask Detection to Prevent the COVID-19 Pandemic. Southeast Conference. Doi: 0.1109/SoutheastCon45413.2021.9401874.
  • Sethi, S., Kathuria, M., Kaushik, T. (2021). Face mask detection using deep learning: An approach to reduce risk of coronavirus spread. Journal of Biomedical Informatics. 120, Doi: 103848. doi.org/10.1016/j.jbi.2021.103848.
  • Sharma Y., Mishra M., Furqan A. (2022). Face Mask Detection Using IoT and Deep Learning for Saftey of Covid-19. International Journal of Engineering, Science, Technology and Innovation (IJESTI), 2, 3.
  • Song, Z, Nguyen, K, Nguyen, T, Cho, C and Gao, J. (2022). Spartan Face Mask Detection and Facial Recognition System. Healthcare. 10, 87. Doi: 0.3390/healthcare10010087.
  • Su, X, Gao, M, Ren, J, Li, Y, Dong, M, Liu, X. (2022). Face mask detection and classification via deep transfer learning. Multimed Tools Appl. 81(3): 4475–4494. Doi: 10.1007/s11042-021-11772-5.
  • Teboulbi, S., Messaoud, S., Hajjaji, MA. and Mtibaa, A. (2021). Real-Time Implementation of AI-Based Face Mask Detection and Social Distancing Measuring System for COVID-19 Prevention. Scientific Programming. Article ID 8340779, 21. 10.1155/2021/8340779.
  • Tomás, J., Rego, A., Viciano-Tudela, S. and Lloret, J. (2021). Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning. Healthcare. 9, 1050. Doi: 10.3390/healthcare9081050.
  • URL 1, https://www.cordeks.com/Page.php?a=pp-meltblown, (Accessed: 21.05.2022).
  • URL 2, https://www.analyticsvidhya.com/blog/2022/01/convolutional-neural-network-an-overview/ (Accession date: 21.05.2022).
  • URL 3, https://maelfabien.github.io/deeplearning/xception, (Accessed: 21.06.2022).
  • Vijayan, T., Sangeetha, M., Karthik, B. (2020). Efficient Analysis of Diabetic Retinopathy on Retinal Fundus Images using Deep Learning Techniques with Inception V3 Architecture. Journal of Green Engineering (JGE) Vol 10, 10.
  • Wakarekar, MM., Gurav, U. (2022). Image Processing and Deep Neural Networks for Face Mask Detection. In: Rajagopal, S., Faruki, P., Popat, K. (eds) Advancements in Smart Computing and Information Security. ASCIS 2022. Communications in Computer and Information Science, vol 1760. Springer, Cham. Doi: 10.1007/978-3-031-23095-0_14

Determining the Reliability of Personal Masks with Convolutional Neural Networks

Year 2024, Volume: 7 Issue: 1, 71 - 85, 29.03.2024

Özgür Boran Ak , Ertan Kuruöz , Ayça Ak

https://doi.org/10.35341/afet.1284220

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.

Keywords

Artificial Intelligence Convolutional Neural Networks Image classification Personal Mask

References

  • Almghraby, M. and Elnady, AO. (2021). Face Mask Detection in Real-Time using MobileNetv2. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958 (Online), Volume-10 Issue-6, pp: 104-108.
  • Asif, S., Wenhui, Y., Tao, Y., Jinhai, S. and Amjad, K. (2021). Real Time Face Mask Detection System using Transfer Learning with Machine Learning Method in the Era of Covid-19 Pandemic. 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), 70-75. Doi: 10.1109/ICAIBD51990.2021.9459008.
  • Bozkurt, F. (2022). A Comparative Study on Classifying Human Activities Using Classical Machine and Deep Learning Methods. Arabian Journal for Science and Engineering 47:1507–1521. Doi: doi.org/10.1007/s13369-021-06008-5
  • Cahvda, A., Dsouza, J., Badgujar S. and Damani A. (2021). Multi-Stage CNN Architecture for Face Mask Detection. 6th International Conference for Convergence in Technology (I2CT) Pune, India. Doi: 10.1109/I2CT51068.2021.9418207.
  • Chen, B., Ju, X., Xiao, B. et al. (2021). Locally GAN-generated face detection based on an improved Xception. Information Sciences Volume 572, September 2021, Pages 16-28.
  • Ciuffreda, S., Picotti, C., Pescio, P. (2021). Medical face masks on the market: Review of materials, characteristics and performed tests. Medical Device Testing. Eurofins Biolab.
  • Daşgın, A., Adem, K. & Kılıçarslan, S. (2023). Detection of Face Mask with Convolutional Neural Network Models to Reduce Covid19 Spread. Journal of the Institute of Science and Technology, 13(3): 1511-1527.
  • Du, X., Cai, Y., Wang, S. and Zhang, L. (2016). Overview of deep learning. 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). 159-164, Doi: 10.1109/YAC.2016.7804882. Fırat, H., Hanbay, D. (2023). Comparison of 3D CNN based deep learning architectures using hyperspectral images, Journal of the Faculty of Engineering and Architecture of Gazi University, 38:1, 521-534.
  • Goyal, H., Sidana, K., Singh, C., et al. (2022). A real time face mask detection system using convolutional neural network. Multimed Tools Applications. 81, 14999–15015 Doi: 10.1007/s11042-022-12166-x.
  • Hariri, W. (2022). Efficient masked face recognition method during the COVID-19 pandemic. SIViP 16, 605–612. https://doi.org/10.1007/s11760-021-02050-w.
  • Hasan, N., Bao, Y., Shawon, A. et al. (2021). DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image. SN COMPUT. SCI. 2, 389. https://doi.org/10.1007/s42979-021-00782-7.
  • Kansal, I., Popli, R. and Singla C. (2021). Comparative Analysis of various Machine and Deep Learning Models for Face Mask Detection using Digital Images. 9th International Conference on Reliability, Infocom Technologies and Optimization. Doi: 10.1109/ICRITO51393.2021.9596407.
  • Kayalı, D., Dimililer, K., Sekeroğlu, B. (2021). Face Mask Detection and Classification for COVID-19 using Deep Learning. International Conference on INnovations in Intelligent SysTems and Applications (INISTA). 10.1109/INISTA52262.2021.9548642.
  • Kaur, G., Sinha, R., Tiwari, PK., Yadav, SK., Pandey, P., Raj, R., Vashisth, A., Rakhra, M. (2022). Face mask recognition system using CNN model. Neuroscience Informatics, 2, 3, 100035. Doi: /10.1016/j.neuri.2021.100035.
  • Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, DJ. (2021). 1D convolutional neural networks and applications: A survey, Mechanical Systems and Signal Processing. 151, 107398. Doi: 10.1016/j.ymssp.2020.107398.
  • Köklü, M., Çınar, I., Taşpınar, YS. (2022). CNN-based bi-directional and directional long-short term memory network for determination of face mask. Biomedical Signal Processing and Control. 71. Doi: 10.1016/j.bspc.2021.103216.
  • Naufal, MF. et al. (2021). Comparative Analysis of Image Classification Algorithms for Face Mask Detection. Journal of Information Systems Engineering and Business Intelligence. Doi: 10.20473/jisebi.7.1.56-66.
  • O’Kelly, E., Arora, A., Pirog, S., Ward, J., Clarkson, PJ. (2021). Comparing the fit of N95, KN95, surgical, and cloth face masks and assessing the accuracy of fit checking. Plos One. 16(1): e0245688. Doi:10.1371/journal.pone.0245688.
  • O’Shea, K., Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv preprint arXiv: 1511.08458.
  • Rajath, AN., Shruthi, BM., Ambareen, K. and Lakshmi, CMS. (2021). An Adaptive Approach to Detect Face Mask in Real Time using Convolutional Neural Network (CNN) Model, 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques, 826-831. Doi: 10.1109/ICEECCOT52851.2021.9708046.
  • Rokhana, R., Herulambang, W., Indraswari, R. (2021). Multi-Class Image Classification Based on MobileNetV2 for Detecting the Proper Use of Face Mask. International Electronics Symposium. Doi: 10.1109/IES53407.2021.9594022.
  • Sighencea, BI., Stanciu, RI., C˘aleanu, CD. (2021). A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction. Sensors, 21, 7543. Doi: 10.3390/s21227543.
  • Snyder, S. and Husari, G. (2021). Thor: A Deep Learning Approach for Face Mask Detection to Prevent the COVID-19 Pandemic. Southeast Conference. Doi: 0.1109/SoutheastCon45413.2021.9401874.
  • Sethi, S., Kathuria, M., Kaushik, T. (2021). Face mask detection using deep learning: An approach to reduce risk of coronavirus spread. Journal of Biomedical Informatics. 120, Doi: 103848. doi.org/10.1016/j.jbi.2021.103848.
  • Sharma Y., Mishra M., Furqan A. (2022). Face Mask Detection Using IoT and Deep Learning for Saftey of Covid-19. International Journal of Engineering, Science, Technology and Innovation (IJESTI), 2, 3.
  • Song, Z, Nguyen, K, Nguyen, T, Cho, C and Gao, J. (2022). Spartan Face Mask Detection and Facial Recognition System. Healthcare. 10, 87. Doi: 0.3390/healthcare10010087.
  • Su, X, Gao, M, Ren, J, Li, Y, Dong, M, Liu, X. (2022). Face mask detection and classification via deep transfer learning. Multimed Tools Appl. 81(3): 4475–4494. Doi: 10.1007/s11042-021-11772-5.
  • Teboulbi, S., Messaoud, S., Hajjaji, MA. and Mtibaa, A. (2021). Real-Time Implementation of AI-Based Face Mask Detection and Social Distancing Measuring System for COVID-19 Prevention. Scientific Programming. Article ID 8340779, 21. 10.1155/2021/8340779.
  • Tomás, J., Rego, A., Viciano-Tudela, S. and Lloret, J. (2021). Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning. Healthcare. 9, 1050. Doi: 10.3390/healthcare9081050.
  • URL 1, https://www.cordeks.com/Page.php?a=pp-meltblown, (Accessed: 21.05.2022).
  • URL 2, https://www.analyticsvidhya.com/blog/2022/01/convolutional-neural-network-an-overview/ (Accession date: 21.05.2022).
  • URL 3, https://maelfabien.github.io/deeplearning/xception, (Accessed: 21.06.2022).
  • Vijayan, T., Sangeetha, M., Karthik, B. (2020). Efficient Analysis of Diabetic Retinopathy on Retinal Fundus Images using Deep Learning Techniques with Inception V3 Architecture. Journal of Green Engineering (JGE) Vol 10, 10.
  • Wakarekar, MM., Gurav, U. (2022). Image Processing and Deep Neural Networks for Face Mask Detection. In: Rajagopal, S., Faruki, P., Popat, K. (eds) Advancements in Smart Computing and Information Security. ASCIS 2022. Communications in Computer and Information Science, vol 1760. Springer, Cham. Doi: 10.1007/978-3-031-23095-0_14
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering, Health Care Administration
Journal Section Articles
Authors

Özgür Boran Ak 0000-0002-2523-7640

Ertan Kuruöz 0000-0002-9628-7418

Ayça Ak 0000-0002-3429-4962

Publication Date March 29, 2024
Acceptance Date March 27, 2024
Published in Issue Year 2024 Volume: 7 Issue: 1

Cite

APA Ak, Ö. B., Kuruöz, E., & Ak, A. (2024). Determining the Reliability of Personal Masks with Convolutional Neural Networks. Afet Ve Risk Dergisi, 7(1), 71-85. https://doi.org/10.35341/afet.1284220
AMA Ak ÖB, Kuruöz E, Ak A. Determining the Reliability of Personal Masks with Convolutional Neural Networks. Afet ve Risk Dergisi. March 2024;7(1):71-85. doi:10.35341/afet.1284220
Chicago Ak, Özgür Boran, Ertan Kuruöz, and Ayça Ak. “Determining the Reliability of Personal Masks With Convolutional Neural Networks”. Afet Ve Risk Dergisi 7, no. 1 (March 2024): 71-85. https://doi.org/10.35341/afet.1284220.
EndNote Ak ÖB, Kuruöz E, Ak A (March 1, 2024) Determining the Reliability of Personal Masks with Convolutional Neural Networks. Afet ve Risk Dergisi 7 1 71–85.
IEEE Ö. B. Ak, E. Kuruöz, and A. Ak, “Determining the Reliability of Personal Masks with Convolutional Neural Networks”, Afet ve Risk Dergisi, vol. 7, no. 1, pp. 71–85, 2024, doi: 10.35341/afet.1284220.
ISNAD Ak, Özgür Boran et al. “Determining the Reliability of Personal Masks With Convolutional Neural Networks”. Afet ve Risk Dergisi 7/1 (March 2024), 71-85. https://doi.org/10.35341/afet.1284220.
JAMA Ak ÖB, Kuruöz E, Ak A. Determining the Reliability of Personal Masks with Convolutional Neural Networks. Afet ve Risk Dergisi. 2024;7:71–85.
MLA Ak, Özgür Boran et al. “Determining the Reliability of Personal Masks With Convolutional Neural Networks”. Afet Ve Risk Dergisi, vol. 7, no. 1, 2024, pp. 71-85, doi:10.35341/afet.1284220.
Vancouver Ak ÖB, Kuruöz E, Ak A. Determining the Reliability of Personal Masks with Convolutional Neural Networks. Afet ve Risk Dergisi. 2024;7(1):71-85.
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