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Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs.

Szilárd Vajda1, Alexandros Karargyris2, Stefan Jaeger3

  • 1Central Washington University, Ellensburg, WA, USA. szilard.vajda@cwu.edu.

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An automated chest X-ray analysis system accurately detects pulmonary tuberculosis (TB). This cost-effective screening tool offers a reliable alternative for high-prevalence regions with limited medical resources.

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Automatic TB screeningAutomatic chest x-ray analysisChest x-rayFeature selectionHOGNeural networksTuberculosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Public Health

Background:

  • Tuberculosis (TB) is a prevalent pulmonary disease, particularly in resource-limited areas like Kenya, often co-occurring with HIV.
  • Traditional diagnostic methods (e.g., sputum smear analysis) are resource-intensive and may be inaccessible in certain regions.
  • Automated analysis of chest radiographs (CXRs) presents a cost-effective and reliable alternative for TB screening.

Purpose of the Study:

  • To develop and evaluate a fully automated system for the detection and classification of pulmonary abnormalities, specifically Tuberculosis (TB), from chest X-rays.
  • To optimize feature selection from a large pool of generic image features for improved classifier performance.
  • To assess the system's efficacy using publicly available datasets and compare its performance against existing state-of-the-art methods and human radiologists.

Main Methods:

  • The system employs image preprocessing techniques to enhance CXR quality, followed by adaptive segmentation to delineate lung regions.
  • A multitude of image features are extracted from the segmented lung regions.
  • A feature selection strategy identifies the optimal subset of features for a neural network classifier, which then determines if the radiograph is normal or abnormal.

Main Results:

  • The automated system achieved a maximum Area Under the Curve (AUC) of 0.99 and an accuracy (ACC) of 97.03% on the Montgomery and Shenzhen CXR datasets.
  • The performance was validated using two large, publicly available chest X-ray collections.
  • Results were benchmarked against current state-of-the-art automated systems and the diagnostic decisions of radiologists.

Conclusions:

  • The developed automated system demonstrates high accuracy and effectiveness in detecting pulmonary Tuberculosis from chest X-rays.
  • This technology offers a scalable and cost-efficient solution for TB screening, particularly beneficial for underserved rural populations in high-prevalence areas.
  • The findings suggest that automated CXR analysis can serve as a valuable tool in global TB control efforts, complementing existing diagnostic strategies.