Abstract
The world has seen the disastrous effect caused by COVID-19 on humankind. The rapidity of COVID-19 transmission, re-infections, post-COVID-19 symptoms, and the emergence of new COVID-19 strands have disrupted the global healthcare systems. Consequently, screening for COVID-19 cases has become of the utmost importance. As temperature and mask checks help significantly to prevent the rapid spread of COVID-19, automating this process in public places has become indispensable. In this work, we propose an end-to-end approach for mask detection followed by temperature for efficient screening. The proposed model achieved 93.5%, 96.7%, and 97.7% precision, recall, and mAP when trained on the thermal surveillance dataset and tested on a lightning dataset consisting of images with varying intensities.
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Srivastava, D.K., Pawan, S.J., Rajan, J. (2022). An Automated Approach for Screening COVID-19 from Thermal Images Using Convolutional Neural Network. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham. https://doi.org/10.1007/978-3-031-19660-7_8
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DOI: https://doi.org/10.1007/978-3-031-19660-7_8
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