Author: Priya, C.; Sithi Shameem Fathima, S.M.H.; Kirubanandasarathy, N.; Valanarasid, A.; Safana Begam, M. H.; Aiswarya, N.
Title: AUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FROM CT IMAGE Cord-id: 7olh1qv8 Document date: 2021_2_5
ID: 7olh1qv8
Snippet: In early 2020, the corona virus disease (COVID-19) has become a global epidemic. The WHO announced the disease as a public health emergency of international importance (PHEIC), and the issue was considered a health emergency. Automated computed tomography (CD) detection of lung infections offers a tremendous opportunity to expand the traditional health approach to resolving COVID-19. But many problems with CT. Facing contaminated areas from fragments, which include greater variability in infecti
Document: In early 2020, the corona virus disease (COVID-19) has become a global epidemic. The WHO announced the disease as a public health emergency of international importance (PHEIC), and the issue was considered a health emergency. Automated computed tomography (CD) detection of lung infections offers a tremendous opportunity to expand the traditional health approach to resolving COVID-19. But many problems with CT. Facing contaminated areas from fragments, which include greater variability in infectious properties and low-intensity comparison between infections and normal tissues. Moreover, by suppressing the project of an in-depth model, a lot of information cannot be collected over some time.
Search related documents:
Co phrase search for related documents- Try single phrases listed below for: 1
Co phrase search for related documents, hyperlinks ordered by date