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Related Experiment Video

Updated: Nov 14, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector

Asu Kumar Singh1, Anupam Kumar1, Mufti Mahmud2

  • 1CSE Department, Maharaja Agrasen Institute of Technology, Delhi, India.

Cognitive Computation
|March 10, 2021
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Summary

This study introduces an AI system for detecting COVID-19 using chest X-rays. The method achieves 99.65% accuracy, offering a vital tool for rapid diagnosis where testing kits are scarce.

Keywords:
Computer-aided detection systemEvolutionary computingFeature reductionSocial group optimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic, caused by SARS-CoV-2, has led to a global health crisis.
  • Limited testing kit availability necessitates alternative diagnostic methods.
  • Chest X-ray (CXR) imaging shows promise for COVID-19 detection.

Purpose of the Study:

  • To develop an intelligent system for detecting COVID-19 infection from CXR images.
  • To enhance diagnostic capabilities, particularly in resource-limited settings.
  • To provide an accurate and efficient alternative to traditional testing.

Main Methods:

  • Feature extraction from CXR images.
  • Feature selection using the Hybrid Social Group Optimization algorithm.
  • Classification of CXR images using various classifiers, including Support Vector Classifier (SVC).

Main Results:

  • The proposed pipeline achieved a high classification accuracy of 99.65% using SVC.
  • This accuracy surpasses that of existing state-of-the-art deep learning algorithms.
  • The system demonstrates effectiveness in both binary and multi-class classification tasks.

Conclusions:

  • The developed pipeline offers a highly accurate method for COVID-19 detection using CXR images.
  • This AI-driven approach can significantly aid radiologists and improve diagnostic accessibility.
  • The system presents a valuable tool for managing the COVID-19 pandemic, especially in remote areas.