Abstract: Covid-19 is an infectious respiratory disorder brought about using a brand-new coronavirus first found in 2019. The severity of symptoms can vary from mild to life-threatening. No vaccine or specific treatment has been developed to address Covid-19. Hence the most effective preventive measure is to practice social distancing and adhere to good hygiene practices. Medical imaging and convolutional neural networks are used in Covid-19 research to quickly identify infected individuals and detect changes in the lung tissue of those infected. Convolutional neural networks can be used to analyze chest CT scans, detecting potential signs of infection like ground-glass opacities, which indicate the presence of Covid-19. This article introduces a powerful framework for classifying COVID-19 images utilizing a hybrid of CNN and an improved version of Gray Wolf Optimizer. To demonstrate the efficiency of the projected framework, it is verified on a standard dataset and compared with other methods, with results indicating its superiority over the others.

Yechun JIN, Guanxiong ZHANG and Jie LI, “Enhancing COVID-19 Diagnosis Through a Hybrid CNN and Gray Wolf Optimizer Framework” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140650

@article{JIN2023,
title = {Enhancing COVID-19 Diagnosis Through a Hybrid CNN and Gray Wolf Optimizer Framework},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140650},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140650},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Yechun JIN and Guanxiong ZHANG and Jie LI}
}