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Pulmonary Tuberculosis IV

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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, 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, 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 Detection in Chest Radiographs Using Spotted Hyena Algorithm Optimized Deep and Handcrafted Features.

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This study introduces an automated method for detecting tuberculosis (TB) from chest X-rays using VGG-UNet and deep learning. The approach achieves over 99% accuracy in identifying TB, aiding early diagnosis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Lung abnormalities, including tuberculosis (TB), are a growing global health concern.
  • Early detection of TB is crucial for effective treatment and preventing mortality.
  • Chest X-rays are a primary diagnostic tool for TB, but interpretation can be challenging.

Purpose of the Study:

  • To develop an automated system for detecting tuberculosis (TB) from chest X-ray images.
  • To enhance the accuracy and efficiency of TB diagnosis using deep learning techniques.
  • To integrate segmentation and classification for improved TB detection.

Main Methods:

  • Utilized VGG-UNet architecture for joint segmentation and classification of TB from X-rays.
  • Employed deep-feature mining, local binary pattern (LBP) feature extraction, and spotted hyena algorithm (SHA) for feature selection.
  • Implemented a pipeline involving image preprocessing, feature extraction, selection, concatenation, and classification using a fine-tree classifier.
  • Trained and validated the model on 3000 chest X-ray images (1500 healthy, 1500 TB).

Main Results:

  • Achieved a classification accuracy exceeding 99% for TB detection.
  • Demonstrated the effectiveness of the VGG-UNet-supported automated procedure.
  • The proposed method shows significant potential for improving TB diagnosis accuracy.

Conclusions:

  • The developed automated system offers a highly accurate (>99%) method for detecting TB from chest X-rays.
  • This approach, combining VGG-UNet, deep features, and SHA, provides a robust tool for early TB identification.
  • The findings suggest a promising advancement in AI-driven medical diagnostics for pulmonary diseases.