Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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AI for Public Health: A Deep Learning and Gradio-Based System for Face Mask Compliance Detection

Authors
Sandip Thite1, 3, *, Srinivas Ambala2, Kalyani Kadam1, Kailas Patil1, Prawit Chumchu4
1Vishwakarma University, Pune, India
2Pimpri Chinchwad College of Engineering, Pune, India
3MIT Academy of Engineering, Pune, India
4Kasetsart University, Sriracha, Thailand
*Corresponding author.
Corresponding Author
Sandip Thite
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_36How to use a DOI?
Keywords
Artificial Intelligence; Deep Learning; Public Health; Face Mask Compliance Detection
Abstract

The COVID-19 pandemic highlighted the urgent need for reliable monitoring of proper face mask usage in public spaces. Manual observation is both labor-intensive and error-prone, making automated solutions essential for safeguarding public health. In this work, we propose a deep learning–based system that detects mask compliance across three categories: correctly worn, incorrectly worn, and no mask. The approach leverages transfer learning with EfficientNet-B0 and a two-phase training strategy enhanced by data augmentation. On a balanced dataset of 8,982 images, the model achieves 97% classification accuracy, with per-class precision and recall exceeding 96%. To ensure practical adoption, the system is deployed through a Gradio-based graphical interface that enables intuitive image uploads, real-time predictions with confidence scores, and CSV export for record-keeping. The lightweight design allows offline operation and is adaptable for integration into surveillance, workplace, or mobile applications. This work demonstrates a feasible and impactful application of AI for public health, offering a robust, user-friendly tool to promote mask-wearing compliance and reduce viral transmission risks. Research works leveraging AI in oncology, categorized across different tracks.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_36How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sandip Thite
AU  - Srinivas Ambala
AU  - Kalyani Kadam
AU  - Kailas Patil
AU  - Prawit Chumchu
PY  - 2026
DA  - 2026/01/06
TI  - AI for Public Health: A Deep Learning and Gradio-Based System for Face Mask Compliance Detection
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 504
EP  - 529
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_36
DO  - 10.2991/978-94-6463-948-3_36
ID  - Thite2026
ER  -