IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Robust Diagnostic System for COVID-19 Based on Chest Radiology Images

Robust Diagnostic System for COVID-19 Based on Chest Radiology Images
View Sample PDF
Author(s): Sasikaladevi N. (School of Computing, SASTRA University (Deemed), India) and Revathi A. (School of EEE, SASTRA University (Deemed), India)
Copyright: 2022
Pages: 16
Source title: Applications of Computational Science in Artificial Intelligence
Source Author(s)/Editor(s): Anand Nayyar (Duy Tan University, Da Nang, Vietnam), Sandeep Kumar (CHRIST University (Deemed), Bangalore, India) and Akshat Agrawal (Amity University, Guragon, India)
DOI: 10.4018/978-1-7998-9012-6.ch003

Purchase

View Robust Diagnostic System for COVID-19 Based on Chest Radiology Images on the publisher's website for pricing and purchasing information.

Abstract

The proposed system is based on a diagnosis of COVID from x-ray images. In the respiratory system, 17 different viral infections are possible. Accurately discriminating COVID from other viral infections is necessary today as it spreads rapidly. The proposed system differentiates COVID infection accurately from other viral infections. The convolutional neural network (CNN) provides superior performance for disease diagnosis based on images in the deep learning era. In this chapter, to solve this issue, the authors propose a hypergraph-based convolutional neural network-based fast and accurate diagnosis system for COVID. In this work, the hypergraph represents the sophisticated features of a lung x-ray image to diagnose COVID. In-depth features are extracted from the x-ray images using residual neural networks. In order to discriminate COVID viral infection from other viral infections, the hypergraph fusion approach is used.

Related Content

Emine Ela Küçük, Dilek Küçük. © 2022. 20 pages.
Çağla Çelemoğlu, Selime Beyza Özçevik, Şenol Eren. © 2022. 23 pages.
Sasikaladevi N., Revathi A.. © 2022. 16 pages.
Sasikaladevi N., Revathi A.. © 2022. 14 pages.
Trieu Minh Vu, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava, Michal Petru. © 2022. 58 pages.
Satheeshkumar B., Sathiyaprasad B.. © 2022. 25 pages.
Ibrahim Furkan Ince. © 2022. 17 pages.
Body Bottom