Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images

Yadav, Anju; Saxena, Rahul; Pal, Vipin; Gupta, Ashray; Arora, Parth; Agarwal, Josh; Diwedi, Anuj

doi:10.1016/b978-0-12-824054-0.00031-9

Artificial Intelligence and Machine Learning for EDGE Computing 2022

DOI: 10.1016/b978-0-12-824054-0.00031-9

|View full text |Cite

Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images

Anju Yadav

Rahul Saxena

Vipin Pal

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Detection and Classification of Covid-19 Using Gray-Level Features and Ensemble Classifier

Patnaik,

Mohanty,

Subudhi

2024

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

The coronavirus or COVID-19 infectious virus is the deadliest and potentially dangerous disease for humans. Radiologists frequently employ medical imaging tools to visualize complex internal structures as well as the functioning of the body. With precise diagnosis, it is possible to identify the infectious COVID-19 virus earlier, especially in an individual having no visible symptoms. For the diagnosis and early detection of the infectious COVID-19 virus, chest X-rays (CXRs) have been utilized which are available at https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database . Applying the gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) feature extraction techniques, the features of the four classes (normal, lung opacity, viral pneumonia, and COVID-19) have been extracted and then classified by utilizing a machine learning (ML) classifier. Six distinct ML classifiers SMO (Sequential Minimal Optimization), Random Tree, MLP (Multi-Layer Perceptron), Linear SVM, Ensemble Classifier (Boosted Tree), and Bayes Net (Bayesian Network) with respective accuracy of 98.85%, 93.19%, 93.35%, 91.5%, 96.4%, and 96.454% are utilized to classify. The classifiers successfully distinguish between normal individuals, viral pneumonia-affected persons, lung opacity individuals, and COVID-19 virus-infected individuals who were considered for the study. These advanced technologies for coronavirus identification may be helpful in areas where access to skilled medical professionals and modern facilities is limited. Hence, as per the analysis, the study may be helpful in disease detection and classification. To classify the virus, radiologists’ second opinion can be quick and accurate in this urgent scenario.

show abstract

Detection and Classification of Covid-19 Using Gray-Level Features and Ensemble Classifier

Patnaik,

Mohanty,

Subudhi

2024

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images

Cited by 1 publication

References 13 publications

Detection and Classification of Covid-19 Using Gray-Level Features and Ensemble Classifier

Detection and Classification of Covid-19 Using Gray-Level Features and Ensemble Classifier

Contact Info

Product

Resources

About