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Related Concept Videos

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Related Experiment Video

Updated: Aug 19, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Optimal Ensemble learning model for COVID-19 detection using chest X-ray images.

S Balasubramaniam1, K Satheesh Kumar1

  • 1Department of Futures Studies, University of Kerala, Thiruvananthapuram, Kerala 695581, India.

Biomedical Signal Processing and Control
|November 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated COVID-19 detection system using chest X-ray analysis. The novel approach combines deep learning and ensemble methods for accurate and precise identification of COVID-19 cases.

Keywords:
COVID-19ClassificationFeature ExtractionOptimizationPreprocessing

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • The COVID-19 pandemic significantly impacted global health, necessitating rapid and accurate diagnostic tools.
  • Radiological imaging, particularly chest radiography, plays a crucial role in identifying COVID-19 related abnormalities.
  • Early detection and localization of affected patients are critical for disease management.

Purpose of the Study:

  • To develop and evaluate a novel, automated system for COVID-19 detection using chest radiograms.
  • To integrate advanced feature extraction techniques with an ensemble classification model for improved diagnostic accuracy.
  • To optimize the neural network component using a meta-heuristic approach for enhanced performance.

Main Methods:

  • A three-step process involving image preprocessing, feature extraction (deep features via Inceptionv3, texture features like Local Vector Patterns and Local Binary Pattern), and classification.
  • An ensemble classification model incorporating Support Vector Machine, Convolutional Neural Network, Optimized Neural Network, and Random Forest.
  • A Self Adaptive Kill Herd Optimization algorithm was employed to fine-tune the neural network weights.

Main Results:

  • The proposed system demonstrated high performance across various metrics, including accuracy, precision, recall, and specificity.
  • The integration of deep and texture features, along with the optimized ensemble model, led to significant improvements in COVID-19 detection.
  • Comparative analysis showed the superiority of the proposed method over conventional approaches.

Conclusions:

  • The developed system offers a promising automated solution for COVID-19 detection from chest radiographs.
  • The hybrid approach combining deep learning, texture analysis, and meta-heuristic optimization effectively enhances diagnostic capabilities.
  • This work contributes to the advancement of artificial intelligence applications in medical diagnostics for infectious diseases.