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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: May 13, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Computer-based quantitative image texture analysis using multi-collinearity diagnosis in chest X-ray images.

Antonio Quintero-Rincón1,2, Ricardo Di-Pasquale1, Karina Quintero-Rodríguez3

  • 1Department of Data Science, Data Science and AI Laboratory, Catholic University of Argentina (UCA), Buenos Aires, Argentina.

Plos One
|April 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for detecting abnormal X-ray images using singular value decomposition (SVD) texture features. A unique "tuning weight" parameter significantly improved classification accuracy for conditions like COVID-19.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Image texture analysis remains a challenge in medical diagnostics.
  • Automated detection of abnormalities in X-ray images is crucial for timely diagnosis.

Purpose of the Study:

  • To develop an automated tool for detecting abnormal X-ray images using tissue attenuation.
  • To enhance the accuracy of X-ray image classification for various lung conditions.

Main Methods:

  • Utilized singular value decomposition (SVD) to extract texture features (singular values, conditional indices).
  • Introduced a "tuning weight" parameter, derived from statistical analysis of SVD variance, to account for tissue attenuation variability.
  • Implemented an ensemble bagged trees classification model for classifying chest X-ray images.

Main Results:

  • Achieved 88% accuracy without the tuning weight and 99% accuracy with its application.
  • Demonstrated significant improvements in standard performance metrics (e.g., accuracy, AUC, true positive rate).
  • Outperformed existing state-of-the-art methods on an imbalanced chest X-ray dataset.

Conclusions:

  • The proposed SVD-based method with a tuning weight parameter is highly effective for automated X-ray image analysis.
  • The tuning weight parameter significantly enhances classification accuracy and reduces misclassification errors.
  • This approach offers a robust solution for diagnosing various lung pathologies from chest X-rays.