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Pre-processing methods in chest X-ray image classification.

Agata Giełczyk1, Anna Marciniak1,2, Martyna Tarczewska1

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Machine learning accurately classifies chest X-rays for COVID-19 and pneumonia detection. Enhancing image pre-processing significantly improves diagnostic performance, aiding medical professionals.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • The COVID-19 pandemic necessitated rapid advancements in diagnostic tools.
  • Chest X-rays are crucial for identifying respiratory illnesses like COVID-19 and pneumonia.
  • Existing diagnostic methods require enhancement for speed and accuracy.

Purpose of the Study:

  • To develop a machine learning model for classifying chest X-ray images.
  • To evaluate the impact of image pre-processing techniques on classification performance.
  • To provide a computational tool to assist medical professionals in diagnosing respiratory conditions.

Main Methods:

  • Implementation of a machine learning algorithm for image classification.
  • Application of pre-processing techniques including thresholding, blurring, and histogram equalization.
  • Validation of the model using chest X-ray datasets for healthy, COVID-19, and pneumonia cases.

Main Results:

  • Achieved high F1-scores: 97% for healthy, 96% for COVID-19, and 99% for pneumonia.
  • Demonstrated significant improvements in accuracy, precision, recall, and F1-scores with enhanced pre-processing.
  • Validated the effectiveness of the machine learning approach in distinguishing between different lung conditions.

Conclusions:

  • Machine learning offers a viable and effective solution for supporting medical professionals in chest X-ray interpretation.
  • Optimizing image pre-processing is critical for maximizing the performance of AI-driven diagnostic tools.
  • This approach has the potential to improve the efficiency and accuracy of diagnosing COVID-19 and other pulmonary diseases.