Publication Type : Journal Article
Publisher : IOP Conference Series: Materials Science and Engineering, IOP Publishing
Source : IOP Conference Series: Materials Science and Engineering, IOP Publishing, Volume 1084, Number 1, p.012001 (2021)
Url : https://doi.org/10.1088/1757-899x/1084/1/012001
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2021
Abstract : The world encountered a deadly disease by the beginning of 2020, known as the coronavirus disease (COVID-19). Among the different screening techniques available for COVID-19, chest radiography is an efficient method for disease detection. Whereas other disease detection techniques are time consuming, radiography requires less time to identify abnormalities caused by the disease in the lungs. In this study, one of the standard deep learning architectures, VGGNet, is modified for classifying chest X-ray images under four categories. The planned model uses images of four classes, namely COVID, bacterial, normal, and viral images. The performance matrices of the planned model are compared with five deep learning architectures, namely VGGNet, AlexNET, GoogLeNET, Inception-v4, and DenseNet-201.
Cite this Research Publication : R. ANAND, Sowmya, V., VIJAYKRISHNAMENON,, Gopalakrishnan, E. A., and Dr. Soman K. P., “Modified Vgg Deep Learning Architecture For Covid-19 Classification Using Bio-Medical Images”, IOP Conference Series: Materials Science and Engineering, vol. 1084, p. 012001, 2021.