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Related Experiment Video

Updated: Aug 30, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.

Shaik Mahaboob Basha1,2, Aloísio Vieira Lira Neto2, Samah Alshathri3

  • 1Department of Electronics and Communication Engineering, Geethanjali Institute of Science and Technology, Nellore, India.

Computational Intelligence and Neuroscience
|August 30, 2022
PubMed
Summary

This study introduces an automated method using chest X-rays (CXRs) to distinguish COVID-19 from viral pneumonia. Machine learning models achieved high accuracy, aiding in rapid diagnosis during the pandemic.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Coronavirus disease (COVID-19) has caused global devastation, necessitating efficient diagnostic tools.
  • Chest X-ray (CXR) imaging offers valuable data for identifying localized infection regions.
  • Automated screening and diagnosis using CXR can significantly aid healthcare systems.

Purpose of the Study:

  • To develop a simple, threshold-based segmentation approach for identifying infection in CXR images.
  • To investigate various texture features (intensity-based, wavelet transform, Laws) for differentiating COVID-19 from viral pneumonia (VP).
  • To implement machine learning classifiers for automated COVID-19 diagnosis.

Main Methods:

  • A threshold-based segmentation approach was used to detect potential infection areas in CXR images.
  • Intensity-based, wavelet transform (WT)-based, and Laws-based texture features were extracted and analyzed.
  • Feature selection was performed using Random Forest (RF), followed by classification using Support Vector Machine (SVM) and RF models.

Main Results:

  • Intensity and WT-based features showed significant variations between COVID-19 and VP.
  • Combined features, trained with SVM and RF classifiers, effectively differentiated between the two conditions.
  • The RF model achieved a high classification accuracy of 0.9 and an Area Under the Curve (AUC) of 0.97.

Conclusions:

  • The implemented methodology demonstrates utility in characterizing and differentiating COVID-19 from VP using CXR.
  • Automated analysis of CXR features shows promise for rapid and accurate diagnosis.
  • Machine learning models, particularly RF, can effectively support clinical decision-making in respiratory infections.