Abstract: This paper introduces a novel deep learning framework for highly accurate COVID-19 detection using chest X-ray images. The proposed model tackles the challenge by combining stacked Convolutional Neural Network models for superior feature extraction to potentially enhance interpretability. The proposed model achieved a high accuracy in distinguishing COVID-19 from healthy cases. The study demonstrates the potential of deep hybrid learning for accurate COVID-19 detection, paving the way for its application in real-world settings. Future research directions could explore methods to further refine the model's capabilities. Overall, this work contributes significantly to the development of robust deep-learning methods for COVID-19 detection with the potential for broader use in medical image analysis.

Mansor Alohali. “Deep Hybrid Learning Approaches for COVID-19 Virus Detection Using Chest X-ray Images”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150711

@article{Alohali2024,
title = {Deep Hybrid Learning Approaches for COVID-19 Virus Detection Using Chest X-ray Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150711},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150711},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Mansor Alohali}
}