Abstract: The Corona-virus spreads too quickly among humans and reaches more than 72 million people around the world until now. To avoid spread, it is very important to recognize the individuals infected. The deep learning (DL) technique for the detection of patients with Corona-virus infection using Chest X-rays (CXR) images is proposed in this article. Besides, we show how to implement an advanced model for deep learning, using chest X-rays (CXR) images, to identify COVID-19 (nCoV-19). The goal is to provide an intellectual image recognition model for over-stressed medical professionals with a second pair of eyes. In using the current publicly available COVID-19 data-sets we emphasize the challenges (including image data-set size and image quality) in developing a valuable deep learning model. We suggest a pre-trained model of a semi-automated image, create a robust image data-set for designing and evaluating a deep learning algorithm. This will provide the researchers and practitioners with a solid path to the future development of an improved model.

Muhammad Ahmed Zaki, Sanam Narejo, Sammer Zai, Urooba Zaki, Zarqa Altaf and Naseer u Din, “Detection of nCoV-19 from Hybrid Dataset of CXR Images using Deep Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111281

@article{Zaki2020,
title = {Detection of nCoV-19 from Hybrid Dataset of CXR Images using Deep Convolutional Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111281},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111281},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Muhammad Ahmed Zaki and Sanam Narejo and Sammer Zai and Urooba Zaki and Zarqa Altaf and Naseer u Din}
}