Real-time Covid-19 diagnosis on embedded IoT platforms

Elmehdi Benmalek; Wajih Rhalem; Atman Jbari; Abdelilah Jilbab; Jamal Elmhamdi

doi:10.35784/iapgos.7616

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Published: Dec 20, 2025

DOI: https://doi.org/10.35784/iapgos.7616

Issue Vol. 15 No. 4 (2025)

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Real-time Covid-19 diagnosis on embedded IoT platforms
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DOI

https://doi.org/10.35784/iapgos.7616

Authors

Elmehdi Benmalek

elmehdi_benmalek@um5.ac.ma

Mohammed V University in Rabat, Morocco

https://orcid.org/0000-0003-1078-1421

Wajih Rhalem

w.rhalem@um5r.ac.ma

Mohammed V University in Rabat, Morocco

https://orcid.org/0000-0001-6221-6833

Atman Jbari

a.jbari@um5r.ac.ma

Mohammed V University in Rabat, Morocco

Abdelilah Jilbab

a_jilbab@yahoo.fr

Mohammed V University in Rabat, Morocco

Jamal Elmhamdi

mhamdi_jamal@yahoo.fr

Mohammed V University in Rabat, Morocco

https://orcid.org/0009-0005-7612-3458

Abstract

COVID-19 continues to pose a persistent global health challenge, where rapid, accurate, and accessible diagnostic tools are crucial for controlling viral transmission. In this study, we present a non-invasive, embedded diagnostic system for COVID-19 detection based on chest CT scan image analysis. The acquired CT images are preprocessed to align with the input dimensions required by four lightweight convolutional neural network (CNN) architectures – ResNet-18, MobileNet, ShuffleNet, and SqueezeNet – selected for their efficiency and suitability in embedded systems. Among these, SqueezeNet achieved a classification accuracy and an f1-score of 99.1%, delivering performance comparable to the other models while offering superior computational efficiency, making it particularly well-suited for real-time, resource-constrained applications. The optimized model was deployed on an NVIDIA Jetson embedded platform to enable on-device, real-time COVID-19 detection at the edge. Diagnostic results are transmitted to the ThingSpeak cloud platform via the MQTT protocol, facilitating continuous, remote health monitoring. Experimental findings confirm the feasibility, accuracy, and real-time operational capability of the proposed embedded system for COVID-19 detection using chest CT scan images.

Keywords:

COVID-19 detection, deep learning, edge computing, embedded systems, IoT health monitoring, CT scan analysis

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Benmalek, E., Rhalem, W., Jbari, A., Jilbab, A., & Elmhamdi, J. (2025). Real-time Covid-19 diagnosis on embedded IoT platforms. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 15(4), 39–45. https://doi.org/10.35784/iapgos.7616

Real-time Covid-19 diagnosis on embedded IoT platforms

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