Automated COVID-19 detection from chest X-ray and CT images using optimised hybrid classifier Online publication date: Fri, 23-Aug-2024
by Madhavi Bhongale; Pauroosh Kaushal; Renu Vyas
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 45, No. 4, 2024
Abstract: Amidst the global threat of infectious diseases, exemplified by COVID-19, conventional RT-PCR detection methods are time-consuming and potentially misleading. This study introduces an innovative approach, utilising CT and X-ray images as markers for efficient COVID-19 detection. An automatic assessment tool, integrating V-SLBT and GLCM features, optimises image texture analysis for precise classification by a deep belief network (DBN). Enhancing accuracy, a hybrid BWUCOA is integrated into DBN. The tool's workflow involves image preprocessing, optimal texture feature computation, and DBN-based classification. Validation with clinical data from 82 patients attests to a 98% accuracy. Comparative analysis reveals a 1.32% improvement for X-ray and a 2.38% enhancement for CT images over existing methods, underscoring the efficacy of V-SLBT and BWUCOA in refining the classifier's accuracy. This swift and cost-effective tool provides a precise diagnosis for COVID-19.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook