Deep Learning Approach for COVID-19 Detection: A Diagnostic Tool Based on VGG16 and VGG19

dc.contributor.author	Akash, Fardin Rahman
dc.contributor.author	Priniya, Ajmiri Afrin
dc.contributor.author	Chadni, Jahani Shabnam
dc.contributor.author	Shuha, Jobaida Ahmed
dc.contributor.author	Emu, Ismot Ara
dc.contributor.author	Reza, Ahmed Wasif
dc.contributor.author	Arefin, Mohammad Shamsul
dc.date.accessioned	2024-05-11T10:10:41Z
dc.date.available	2024-05-11T10:10:41Z
dc.date.issued	2020-12-20
dc.identifier.uri	http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12331
dc.description.abstract	The coronavirus disease 2019 is a new contagious illness affecting the lungs and upper respiratory tract. It has various complications that can affect the quality of life. One of the most common factors that can be used to diagnose this illness is chest computed radiography. According to studies, deep learning can identify COVID-19 using chest radiography results. We created a CNN network to find COVID-19 in patients with Pneumonia and normal controls after a full chest X-ray. For the testing and training of the VGG16 model, we focused on its deep features. The study's results revealed that the VGG16 model had the highest accuracy score, at 70.0021%.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Springer	en_US
dc.subject	Coronavirus disease	en_US
dc.subject	Covid-19	en_US
dc.subject	Vaccination	en_US
dc.subject	Deep learning	en_US
dc.title	Deep Learning Approach for COVID-19 Detection: A Diagnostic Tool Based on VGG16 and VGG19	en_US
dc.type	Article	en_US