Vol. 21 No. 1 (2025), Articles
Vol. 21 No. 1 (2025)

Deep-Learning-Based Mobile Application for Detecting COVID-19

Articles
Received 2024-03-18,
Revised 2024-12-01,
Accepted 2024-12-22,
Published 2025-03-01
Noor Kamal Al-Qazzaz
Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Alaa A. Aldoori
Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Tabarak Emad Hussein
Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Massarra Mohammed Mahdi
Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Sawal Hamid Bin Mohd Ali
Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600/ Malaysia
Siti Anom Ahmad
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
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Keywords

Deep learning, CNN, MobileNet-V2, image processing, COVID-19, lung opacity, mobile display

How to Cite

Deep-Learning-Based Mobile Application for Detecting COVID-19. (2025). Al-Khwarizmi Engineering Journal, 21(1), 13-27. https://doi.org/10.22153/kej.2025.12.001
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Abstract

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated using precision, sensitivity, specificity, accuracy, and F-measure to classify CXR images into COVID-19, non-COVID-19 lung opacity, and normal control. Results showed a precision of 92.91%, sensitivity of 90.6, specificity of 96.45%, accuracy of 90.6%, and F-measure of 91.74% in COVID-19 detection. Indeed, the suggested MobileNetV2 deep-learning CNN model can improve classification performance by minimising the time required to collect per-image results for a mobile application.

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