COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019370" target="_blank" >RIV/62690094:18470/22:50019370 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/cmc/v72n1/46856" target="_blank" >https://www.techscience.com/cmc/v72n1/46856</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2022.023418" target="_blank" >10.32604/cmc.2022.023418</a>

Jazyk výsledku

angličtina

Název v původním jazyce

COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Popis výsledku v původním jazyce

Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is used (MWOA) for a feature selection algorithm. The severity of lung infection is detected on the basis of age group. Fourth, image classification is done by using the proposed MWOA with the salp swarm algorithm (MWOA-SSA). MWOA-SSA has an accuracy of 97%, whereas PCA and MWOA have accuracies of 81% and 86%. The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA (84.4%) and MWOA (95.2%). MWOA-SSA outperforms other algorithms with a specificity of 97.8%. This proposed method improves the effective classification of lung affected images from large datasets.

Název v anglickém jazyce

COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Popis výsledku anglicky

Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is used (MWOA) for a feature selection algorithm. The severity of lung infection is detected on the basis of age group. Fourth, image classification is done by using the proposed MWOA with the salp swarm algorithm (MWOA-SSA). MWOA-SSA has an accuracy of 97%, whereas PCA and MWOA have accuracies of 81% and 86%. The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA (84.4%) and MWOA (95.2%). MWOA-SSA outperforms other algorithms with a specificity of 97.8%. This proposed method improves the effective classification of lung affected images from large datasets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

CMC-Computers, Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Svazek periodika

72

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1685-1698

Kód UT WoS článku

000767341900011

EID výsledku v databázi Scopus

2-s2.0-85125372115