All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    CMC-Computers, Materials &amp; Continua

  • ISSN

    1546-2218

  • e-ISSN

    1546-2226

  • Volume of the periodical

    72

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    14

  • Pages from-to

    1685-1698

  • UT code for WoS article

    000767341900011

  • EID of the result in the Scopus database

    2-s2.0-85125372115