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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, 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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Vit-Ensemble: Probabilistic voting based ensemble of Vision Transformers for tuberculosis detection using

Nitesh Pradhan1, Gaurav Srivastava2, Geetika Kaushik2

  • 1Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, 302031, Rajasthan, India.

Computational Biology and Chemistry
|November 22, 2025
PubMed
Summary

This study introduces Vit-Ensemble, a novel model for tuberculosis (TB) detection using chest X-ray (CXR) images. Vit-Ensemble achieves 99.67% accuracy by combining multiple Vision Transformer (ViT) models through probabilistic voting, improving early diagnosis.

Keywords:
Chest X-rayEnsemble learningMedical imagingProbabilistic votingTuberculosis detectionVision TransformersVit-Ensemble

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Tuberculosis (TB) detection from chest X-ray (CXR) images is crucial for global health.
  • Existing methods face challenges in accuracy and generalization.

Purpose of the Study:

  • To develop a robust and accurate model for TB detection from CXR images.
  • To leverage Vision Transformer (ViT) architectures and ensemble learning for improved diagnostic performance.

Main Methods:

  • Developed Vit-Ensemble, an ensemble model using multiple ViT architectures.
  • Implemented a probabilistic voting strategy by averaging class probabilities from individual ViT models.
  • Explored various image preprocessing techniques, including contrast enhancement and noise reduction.

Main Results:

  • Vit-Ensemble achieved a high accuracy of 99.67% on benchmark datasets.
  • The model outperformed individual ViT components (DeiT-Base: 99.14%) and state-of-the-art CNNs (EfficientNet-B3: 99.64%, DenseNet201: 93.21%).
  • Probabilistic voting enhanced generalization and reduced model bias.

Conclusions:

  • Vit-Ensemble demonstrates superior performance for TB detection compared to existing methods.
  • The study highlights the effectiveness of probabilistic voting in ensemble learning for medical image analysis.
  • This advancement offers potential for earlier TB diagnosis and improved disease management.