Deep Learning-Powered Mobile App for Fast and Accurate COVID-19 Detection from Chest X-rays

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.

@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},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Rahhal Errattahi and Fatima Zahra Salmam and Mohamed Lachgar and Asmaa El Hannani and Abdelhak Aqqal},
  doi       = {10.14569/IJACSA.2023.01411127},
  url       = {https://doi.org/10.14569/IJACSA.2023.01411127}
}