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Pulmonary Tuberculosis IV01:26

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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
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Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
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Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial

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Summary

This study introduces an automated system for detecting tuberculosis (TB) using deep learning on chest X-rays (CXRs). Segmenting lung images significantly improved TB detection accuracy, outperforming raw image analysis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • Tuberculosis (TB) is a fatal infectious disease requiring accurate diagnosis.
  • Manual interpretation of chest X-rays (CXRs) for TB diagnosis can be subjective.
  • Automated CXR analysis offers a potentially reliable alternative for TB detection.

Purpose of the Study:

  • To develop and evaluate an automated deep learning (DL) system for tuberculosis detection using chest X-rays (CXRs).
  • To investigate the impact of image segmentation on the performance of DL models for TB classification.
  • To utilize explainable artificial intelligence (XAI) for visualizing TB-infected lung regions.

Main Methods:

  • Proposed an automated TB detection system employing deep learning (DL) models.
  • Utilized sophisticated segmentation networks to extract relevant regions of interest from CXRs.
  • Fed segmented CXR images into various convolutional neural network (CNN) models for classification.
  • Employed explainable artificial intelligence (XAI) for visual interpretation of TB indicators.
  • Evaluated model performance on three public CXR datasets.

Main Results:

  • The EfficientNetB3 model achieved a high accuracy of 99.1% and a receiver operating characteristic (ROC) of 99.9%.
  • The system demonstrated an average accuracy of 98.7% across experiments.
  • Performance analysis confirmed that using segmented lung CXR images yielded superior results compared to raw images.

Conclusions:

  • Automated TB detection using DL on segmented CXR images is highly accurate and reliable.
  • Image segmentation is a crucial preprocessing step for enhancing DL model performance in TB diagnosis.
  • The developed system, particularly EfficientNetB3, shows significant promise for clinical application in TB screening.