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Related Experiment Video

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Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification.

Samy Bakheet1, Ayoub Al-Hamadi2

  • 1Faculty of Computers and Information, Sohag University, P.O. Box 82533, Sohag, Egypt; Institute for Information Technology and Communications (IIKT) Otto-von-Guericke-University Magdeburg, D-39106, Magdeburg, Germany.

Computers in Biology and Medicine
|August 29, 2021
PubMed
Summary

This study introduces an automated framework for detecting COVID-19 from chest X-rays using texture features and a machine learning model. The system achieves high accuracy, aiding in rapid and reliable COVID-19 diagnosis.

Keywords:
COVID-19Computer-aided detectionCross-validationGLCM-Based texture featuresLatent-dynamic conditional random fieldsRadiological images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Computer-aided detection (CAD) for COVID-19 using radiological images is crucial for rapid diagnosis.
  • Existing CAD frameworks require enhancement for speed, reliability, and accuracy.

Purpose of the Study:

  • To present an innovative framework for automatic COVID-19 detection from chest X-ray (CXR) images.
  • To leverage Gray Level Co-occurrence Matrix (GLCM) based textural features for robust lung tissue pattern representation.

Main Methods:

  • Image preprocessing including spatial filtering, median filtering, and contrast limited adaptive histogram equalization.
  • Automatic lung region segmentation using Otsu's method for thresholding.
  • Feature extraction using GLCM and classification with a Latent-Dynamic Conditional Random Fields (LDCRFs) model.

Main Results:

  • The framework achieved high performance on a large CXR dataset.
  • Average accuracy of 95.88%, precision of 96.17%, recall of 94.45%, and F1-score of 95.79%.

Conclusions:

  • The proposed framework demonstrates effective automatic COVID-19 detection from CXR images.
  • The method shows competitive or superior performance compared to existing literature, aiding radiologists.