Abstract
Nowadays, the detection of the disease that is called Coronavirus or COVID-19 is essential for the whole world. Scientific researchers have spent significant efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose and treat COVID-19. Convolutional neural networks (CNNs), have obtained remarkable results in numerous applications. One of these applications is image classification. Chest radiograph (X-ray) images can be requested for early COVID-19 classification of patients. Hence, this paper makes demonstrates different CNN architectures utilizing Chest radiograph database images for COVID-19: detection ( Kaggle’s X-ray chest images). It contains three different classes of images: 1) COVID-19, 2) normal, and 3) viral pneumonia Chest radiograph images. Therefore, three alternative CNN architectures like SqueezeNet, GoogleNet, and ResNet 50 have been realized using Matlab 2019a and numerical simulation has been performed. GoogleNet has achieved good performance based on the accuracy obtained with a value of 97.02% and it saves time-consuming. A performance comparison between different techniques has been carried out and this comparison shows that the detection is accurate enough for the non-uniform structure of the chest radiograph images.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Electrical Engineering and Computer Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.