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Related Experiment Video

Updated: Nov 1, 2025

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Detecting pulmonary diseases using deep features in X-ray images.

Pablo Vieira1,2, Orrana Sousa1, Deborah Magalhães3

  • 1Electrical Engineering Department, Federal University of Piau, Picos, Brazil.

Pattern Recognition
|June 21, 2021
PubMed
Summary

Deep learning models accurately detect COVID-19 pneumonia from chest X-rays. This AI approach, using advanced image processing, significantly aids in rapid and precise diagnosis, outperforming existing methods.

Keywords:
COVID-19Deep learningPre-processingX-ray

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • COVID-19 presents with lower respiratory tract lesions visible on chest X-rays.
  • Accurate and rapid diagnosis of COVID-19 pneumonia is crucial for patient management.

Purpose of the Study:

  • To investigate deep learning architectures for detecting COVID-19 pneumonia in X-ray images.
  • To propose an optimized image resizing method for improved classification accuracy.

Main Methods:

  • Utilized seven deep learning architectures combined with data augmentation and transfer learning.
  • Implemented a novel image resizing method using the maximum window function.
  • Trained and evaluated models on chest X-ray images classifying COVID-19, normal, viral, and bacterial pneumonia.

Main Results:

  • Achieved high accuracy (99.8%) in classifying COVID-19, normal, and other pneumonia types.
  • Differentiated COVID-19 from viral pneumonia with 99.8% accuracy and from bacterial pneumonia with 99.9% accuracy.
  • The proposed image resizing method enhanced classification performance across all tested deep learning models.

Conclusions:

  • Deep learning models, when trained with pre-processed X-ray images, can precisely assist specialists in COVID-19 detection.
  • The developed method demonstrates superior performance compared to existing state-of-the-art approaches.
  • This AI-driven approach offers a promising tool for efficient and accurate COVID-19 screening.