Identification of COVID-19 using chest X-Ray images

ABSTRACT

SARS-COV2 or the coronavirus disease even after two years of identification is still a problem because of its varying mutations. The prediction and classification of the virus are one of the most important tasks for controlling the spread of the pandemic. So immediate development of some strong techniques with artificial intelligence implementation to predict COVID-19 is required. The paper’s main aim is to develop an accurate, efficient, and time-saving model for detecting COVID-19 from Chest X-Ray (CXR) images. This study utilises a dataset from the Kaggle database containing CXR images of COVID-19, Viral Pneumonia, and Normal Healthy lung images. Here, some Deep Learning (DL) models like GoogleNet, SqueezeNet, and ResNet-18 are utilised for the detection of the virus. The overall accuracy achieved from the models like GoogleNet is 92.46%, for SqueezeNet is 92.57%, and ResNet-18 is 97.38%. From the obtained outcomes it is concluded that ResNet-18 provides accurate results as compared with the other three models. The achieved outcomes could help medical experts in the future to predict and classify COVID-19 and can very quickly diagnose cases with effective models.

KEYWORDS:

• Deep Learning
• Chest X-ray images
• GoogleNet
• SqueezeNet
• Residual Networks

Disclosure statement

The authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report.

Additional information

Notes on contributors

Vijaya Patnaik

Asit Kumar Subudhi received B.E. in Electronics & Communication in 2005 and M. Tech in Communication System Engineering from BPUT, Bhubaneswar, in 2010, and Ph.D. in Electronic Engineering at SOA deemed to be University in 2018. Presently working as an Associate Professor in the Department of Electronics & Communication Engineering, SOA University, Odisha, India. He has over 18 years of experience in teaching and his research expertise focuses on Biomedical signal and image processing, and VLSI design. He is a member of IEEE. Email: [email protected]

Monalisa Mohanty

Monalisa Mohanty received M. Tech in Communication System Engineering from BPUT, Bhubaneswar, in 2010 and Ph.D. in Electronics Engineering at SOA University in 2019. Currently working as an Associate Professor in the Electronics and Communication Engineering department, at ITER in SOA deemed to be University. She is having a total of 15 years of experience. Her area of interest is Biomedical signal and image processing research. She has also bagged publications in many reputed journals and conferences. Email: [email protected]

Asit Kumar Subudhi

Vijaya Patnaik received M. Tech in Electronics and Instrumentation Engineering from CET, Bhubaneswar, in 2020 and continuing her Ph.D. in Electronics Engineering at SOA University since 2020. Her area of interest is Biomedical signal and image processing research. She has also bagged publications in many journals and conferences.Email: [email protected]