English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The pneumonia caused by Coronavirus disease (COVID-19) is one of major globalthreat and a number of detection and treatment procedures are suggested by theresearchers for COVID-19. The proposed work aims to suggest an automated imageprocessing scheme to extract the COVID-19 lesion from the lung CT scan images(CTI) recorded from the patients. This scheme implements the followingprocedures; (i) Image pre-processing to enhance the COVID-19 lesions, (ii)Image post-processing to extract the lesions, and (iii) Execution of a relativeanalysis between the extracted lesion segment and the Ground-Truth-Image (GTI).This work implements Firefly Algorithm and Shannon Entropy (FA+SE) basedmulti-threshold to enhance the pneumonia lesion and implementsMarkov-Random-Field (MRF) segmentation to extract the lesions with betteraccuracy. The proposed scheme is tested and validated using a class of COVID-19CTI obtained from the existing image datasets and the experimental outcome isappraised to authenticate the clinical significance of the proposed scheme. Theproposed work helped to attain a mean accuracy of >92% during COVID-19 lesionsegmentation and in future, it can be used to examine the real clinical lungCTI of COVID-19 patients.

Firefly-Algorithm Supported Scheme to Detect COVID-19 Lesion in Lung CT Scan Images using Shannon Entropy and Markov-Random-Field