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Related Concept Videos

Pulmonary Tuberculosis IV01:26

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.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
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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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Pulmonary Tuberculosis V01:28

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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, 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.
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Leveraging transfer learning techniques for automated tuberculosis classification on chest X-rays.

Kalyani P Karule1, Vinod Sapkal2, Gayatri Mirajkar3

  • 1Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India.

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Summary

A new hybrid deep learning framework, Domain-Adversarial Transfer Learning with Label Smoothing (DANN-LS), improves tuberculosis (TB) detection from chest X-rays. This automated approach enhances accuracy and reliability in resource-limited settings.

Keywords:
Chest X-raysDeep learningDomain adaptationLabel smoothingTransfer learningTuberculosis detection

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Public Health

Background:

  • Tuberculosis (TB) poses a significant global health challenge, especially in resource-limited areas with limited diagnostic capabilities.
  • Chest X-ray screening for TB is widely used but relies on subjective radiologist interpretation, leading to variability and potential errors.
  • Existing deep learning models struggle with TB classification due to domain shifts and noisy labels in medical datasets.

Purpose of the Study:

  • To develop an automated, reliable method for TB detection using chest X-rays that overcomes limitations of traditional transfer learning.
  • To address challenges of domain variation and label noise in deep learning models for TB classification.

Main Methods:

  • Proposed a hybrid framework, Domain-Adversarial Transfer Learning with Label Smoothing (DANN-LS), integrating domain-invariant feature learning and label regularization.
  • Utilized a ResNet50 architecture with a gradient reversal layer for adversarial domain alignment and label smoothing to prevent overconfident predictions.
  • Extensively preprocessed and augmented images from the Tuberculosis Chest X-ray dataset for robust model training.

Main Results:

  • The DANN-LS model achieved high performance metrics: 93.5% classification accuracy, 97.2% AUC, 92.8% F1-Score, and 93.9% sensitivity and specificity.
  • Demonstrated a ~5% improvement over standard transfer-learning methods and significant gains compared to Wasserstein-based and classical adversarial domain-adaptation techniques.
  • Empirical findings confirm the effectiveness of adversarial domain adaptation and label smoothing for secure TB screening.

Conclusions:

  • The DANN-LS framework offers a robust and scalable solution for accurate TB screening, particularly valuable in resource-constrained environments.
  • Adversarial domain adaptation combined with label smoothing effectively addresses domain shift and label noise in medical image analysis.
  • This automated approach has the potential to significantly enhance public health responses to tuberculosis.