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Updated: May 20, 2025

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Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest

K Vanitha1, T R Mahesh2, V Vinoth Kumar3

  • 1Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore, India.

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|March 25, 2025
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Summary

This study introduces a novel Vision Transformer (ViT) model with Gradient-weighted Class Activation Mapping (Grad-CAM) for improved tuberculosis diagnosis from chest X-rays. The AI model achieves high accuracy, aiding radiologists in early detection.

Keywords:
Chest X-raysConvolutional neural networksDeep learningDiagnostic accuracyExplainable AIGrad-CAMMedical imagingSelf-attentionTuberculosis detectionVision Transformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Tuberculosis (TB) diagnosis from chest X-rays is crucial but challenging due to subtle disease manifestations.
  • Traditional methods using Convolutional Neural Networks (CNNs) require extensive pre-processing and lack generalizability.
  • Accurate and interpretable AI tools are needed for early TB detection, especially in resource-limited settings.

Purpose of the Study:

  • To develop and evaluate a novel Vision Transformer (ViT) model integrated with Gradient-weighted Class Activation Mapping (Grad-CAM) for enhanced TB diagnosis from chest X-rays.
  • To improve diagnostic accuracy and model interpretability compared to existing computational methods.
  • To facilitate clinical application and assist radiologists in automated TB detection.

Main Methods:

  • A Vision Transformer (ViT) model with a Conv2D stem and transformer encoder blocks was developed to process raw X-ray pixels directly.
  • Self-attention mechanisms in the ViT model were utilized to capture long-range dependencies and complex patterns.
  • Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated to provide visual explanations of the model's diagnostic decisions.

Main Results:

  • The ViT-Grad-CAM model achieved high performance on validation and test sets, with accuracy, recall, and F1-scores consistently above 0.97.
  • The model demonstrated superior performance compared to existing methods for TB detection in chest X-rays.
  • Grad-CAM provided visual heatmaps highlighting significant regions, enhancing model transparency and aiding radiologist verification.

Conclusions:

  • The novel ViT-Grad-CAM model offers a significant improvement in automated tuberculosis detection from chest X-rays.
  • The model's high accuracy and interpretability show strong potential for clinical application in real-world settings.
  • This approach enhances diagnostic precision and supports radiologists in timely and accurate TB diagnosis.