A Convolutional Neural Network Model and Software Tool for Classifying the Presence of a Medical Mask on a Human Face

Y.S. Hryhorenko, V.M. Shymkovysh, P.I. Kravets, A.O. Novatskyi, L.L. Shymkovysh, A.Yu. Doroshenko

Abstract


A model of a convolutional neural network, a database for training a neural network, and a software tool for classifying the presence of a medical mask on a person’s face, which allows recognizing the presence of a medical mask from the transmitted image, have been developed. The structure of the neural network model was optimized to improve classification results. In addition, the development of the user interface was carried out. The developed application was tested on a set of random images. The resulting model demonstrated high accuracy and robustness in solving the task of classifying the presence of a medical mask on a person’s face, which allows automating measures to protect people from the spread of diseases. The implemented application meets the requirements for speed and quality of classification. Further improvement of the classification quality of CNN can be done by collecting a larger dataset and researching other CNN architectures.

Problems in programming 2023; 2: 59-66


Keywords


convolutional neural networks; image classification; Python; tensorflow; keras; medical masks

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References


Dreyfus G (2005) Neural networks: methodology and applications. Springer-Verlag, Berlin. CrossRef

Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE.( 2017) A survey of deep neural network architectures and their applications. Neurocomputing. vol. 234, pp. 11-26. CrossRef

Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar S. (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv (CSUR).vol. 51, no. 5, pp. 1-36. CrossRef

Z. Q. Zhao, P. Zheng, S. T. Xu and X. Wu. (2019) Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212-3232. CrossRef

Shymkovych V., Telenyk S., Kravets P. (2021) Hardware implementation of radial- basis neural networks with Gaussian activation functions on FPGA. Neural Computing and Applications. vol. 33, no.15, pp. 9467- 9479. CrossRef

Tian H, Chen SC, Shyu ML.(2020) Evolu- tionary programming based deep learning feature selection and network construction for visual data classification. Inf Syst Front, vol. 22, no. 5, pp. 1053-1066. CrossRef

Bezliudnyi Y., Shymkovysh V., Doroshenko A.( 2021) Convolutional neural network model and software for classification of typical pests. Prombles in programming. vol.4, pp. 95-102. CrossRef

Kravets P., Nevolko P., Shymkovych V., Shymkovych L. (2020) Synthesis of High-Speed Neuro-Fuzzy-Controllers Based on FPGA. 2020 IEEE 2nd International Con- ference on Advanced Trends in Information Theory (ATIT). pp. 291-295. CrossRef

Shymkovych, Volodymyr, Anatoliy Doroshenko, Tural Mamedov, and Olena Yatsenko (2022) Automated Design of an Artificial Neuron for Field-Programmable Gate Arrays Based on an Algebra-Algorithmic Approach. International Scientific Technical Journal «Problems of Control and Informatics» vol. 67, no. 5, pp. 61-72. CrossRef

Dhillon A, Verma GK. (2020) Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif Intell. vol.9, no. 2, pp. 85-112. CrossRef

Khan A, Sohail A, Zahoora U, Qureshi AS. (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev. vol. 53, no. 8, pp. 5455-5501. CrossRef

Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, vol. 8, no. 53, pp. 1-74. CrossRef

Khan, A., Sohail, A., Zahoora, U. et al. (2020) A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, vol. 53, pp. 1-62. CrossRef

Tabian I, Fu H, Sharif Khodaei Z. A. (2019) Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures. Sensors, vol. 19, №22:4933. CrossRef

Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767.

Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv:2004.10934.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in neural information processing systems (pp. 1097-1105).

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. CrossRef

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). CrossRef

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 1-48. CrossRef

Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International Conference on Machine Learning (pp. 6105-6114).

TensorFlow. (n.d.). TensorFlow: An end-to-end open source machine learning platform. Retrieved from https://www.tensorflow.org/

Keras. (n.d.). Keras: The Python deep learn- ing API. Retrieved from https://keras.io

Redmon, J. (n.d.). Darknet: Open source neural networks in C. Retrieved from https://pjreddie.com/darknet

COCO Dataset. (n.d.). Common Objects in Context. Retrieved from https://cocodataset.org/

ImageNet. (n.d.). ImageNet: A large-scale hierarchical image database. Retrieved from http://www.image-net.org/

OpenCV. (n.d.). OpenCV: Open source computer vision library. Retrieved from https://opencv.org/

Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A.A. (n.d.). Albumentations: Fast image augmentation library. Retrieved from https:// github.com/albumentations-team/albumentations




DOI: https://doi.org/10.15407/pp2023.02.059

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