Author: Ata, M. M.
Title: A Robust Estimation for the Severity Rate of COVID-19 through CT Scans Using Neutrosophic Sets Cord-id: 2wrikcok Document date: 2021_1_1
ID: 2wrikcok
Snippet: The presented study introduces a robust estimation for the rate of existence of Coronavirus disease (COVID-19) in lungs through CT images in the state of the art of neutrosophic sets. Accordingly, identifying the severity rate of pneumonia infection in the lungs will be easily utilized. The main methodology depends on isolating the non-infected areas in both lungs by using neutrosophic set (NS) algorithm in order to segment these areas. The proposed methodology aims to extract three major sets o
Document: The presented study introduces a robust estimation for the rate of existence of Coronavirus disease (COVID-19) in lungs through CT images in the state of the art of neutrosophic sets. Accordingly, identifying the severity rate of pneumonia infection in the lungs will be easily utilized. The main methodology depends on isolating the non-infected areas in both lungs by using neutrosophic set (NS) algorithm in order to segment these areas. The proposed methodology aims to extract three major sets of images;truth, falsity, and indeterminacy. Moreover, the resultant three images pass through a collection of image processing algorithms in order to increase the segmentation accuracy. Experimental results show a superior segmentation efficacy which guarantees a suitable decision making of concerning the severity rate. © 2021 IEEE.
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