Abstract: The COVID-19 pandemic has imposed significant challenges on healthcare systems globally, necessitating swift and precise screening methods to curb transmission. Traditional screening approaches are time-consuming and prone to errors, prompting the development of an innovative solution - a mobile application employing machine learning for automated COVID- 19 screening. This application harnesses computer vision and deep learning algorithms to analyze X-ray images, rapidly de-tecting virus-related symptoms. This solution aims to enhance the accuracy and speed of COVID-19 screening, particularly in resource-constrained or densely populated settings. The paper details the use of convolutional neural networks (CNNs) and transfer learning in diagnosing COVID-19 from chest X-rays, highlighting their efficacy in image classification. The trained model is deployed in a mobile application for real-world testing, aiming to aid healthcare professionals in the battle against the pandemic. The paper provides a comprehensive overview of the background, methodology, results, and the application’s architecture and functionalities, concluding with avenues for future research.

Rahhal Errattahi, Fatima Zahra Salmam, Mohamed Lachgar, Asmaa El Hannani and Abdelhak Aqqal, “Deep Learning-Powered Mobile App for Fast and Accurate COVID-19 Detection from Chest X-rays” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01411127

@article{Errattahi2023,
title = {Deep Learning-Powered Mobile App for Fast and Accurate COVID-19 Detection from Chest X-rays},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411127},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411127},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Rahhal Errattahi and Fatima Zahra Salmam and Mohamed Lachgar and Asmaa El Hannani and Abdelhak Aqqal}
}