Hybrid Deep Learning Model for Automated Tuberculosis Detection from Chest X-Rays

Abstract

Tuberculosis (TB) resides a significant health challenge, particularly in regions with limited access to medical resources. It spreads through the air and mainly affects the lungs, and without early detection, it can be deadly. Unfortunately, many areas don’t have enough trained healthcare workers or proper screening tools, which delays diagnosis.To help with this issue, we’re currently working on building an AI-based system that can automatically detect TB using chest X-ray images. Our approach involves a multi model deep learning system that combines two architectures: DeiT (Data-efficient Vision Transformer) and ResNet-16. DeiT helps the model understand the overall structure of the image through attention mechanisms, while ResNet-16 focuses on capturing detailed features in specific regions, all while keeping the system lightweight and efficient. We\'re using the TBX11K dataset , featuring three types of chest X-rays: healthy, non-TB but sick, and TB-positive. To make the system more transparent, we’re also adding heatmap visualizations using class activation mapping, so we can actually see which parts of the image the model is focusing on while making predictions. Our target is to design a model that’s not only accurate—hopefully hitting above 99% in key metrics—but also fast, with a prediction time of under 5 milliseconds. Once complete, we aim to make it suitable for real-time use in hospitals and clinics, especially in areas where medical support is limited.

Introduction

• Tuberculosis (TB) remains a critical global health threat, with more deaths than HIV/AIDS in 2022 and ranking behind only COVID-19 among infectious diseases.

• Despite improvements in treatment, early diagnosis is a major challenge, especially in low-resource settings due to limited diagnostic tools and trained professionals.

• Deep Learning (DL) and Artificial Intelligence (AI), particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for automated TB detection from chest X-rays.

2. Problem & Challenges

• Variability in image quality, lack of large annotated datasets, and overfitting remain common issues.

• Existing DL models often struggle to generalize across different machines and populations.

• There’s a need to balance accuracy, speed, and interpretability — a difficult trade-off in medical AI applications.

3. Proposed Solution

• The study introduces a hybrid model combining ResNet-16 and DeiT (Data-efficient Vision Transformer):

  • ResNet-16 handles fine-grained local features (e.g., lesions, cavities).

  • DeiT captures global contextual features using attention mechanisms.

  • The architecture is both accurate and lightweight, ideal for clinical use in low-resource environments.

• Uses Class Activation Maps (CAMs) for visual explanation, aiding clinicians in understanding model decisions.

4. Dataset & Preprocessing

• TBX11K dataset is used, consisting of three classes: healthy, non-TB illnesses, and TB-positive cases.

• Preprocessing includes:

  • Cleaning artifacts

  • Data augmentation (flip, zoom, rotate, shift) for robustness

• Split: 80% train, 10% validation, 10% test.

5. Training & Optimization

• Optimized with Adam optimizer; learning rate: 0.001

• Dropout (0.2) prevents overfitting

• Early stopping conserves computation

• Feature extraction uses Global Average Pooling (GAP)

• CAMs are used for interpretability

6. Performance Metrics

Evaluated using:

• Accuracy

• Precision

• Recall (Sensitivity)

• Specificity

• Inference time

• Parameter count

The model achieves >99% accuracy, is fast, and has a relatively low parameter count — making it suitable for real-time clinical deployment.

7. Comparative Evaluation

• Compared against VGG16, DenseNet, and ResNet-based models.

• Outperforms others in accuracy, precision, and recall, while being more efficient and generalizable.

8. Literature Review Highlights

• Various techniques explored in other studies:

  • Stochastic ANNs, DenseNet with attention (CBAM), segmentation approaches, MobileNet with optimization algorithms (AEO), and stacked CNN ensembles.

  • Most models are accurate but often computationally intensive, lack explainability, or rely on large labeled datasets, limiting practical deployment.

  • Many fail to generalize across diverse clinical settings or equipment variability.

9. Discussion & Limitations

• The hybrid ResNet–DeiT model effectively captures both local and global features, improving TB detection accuracy and clinical interpretability.

• Heatmaps help build trust among healthcare professionals.

• Limitations:

  • Still requires high computing power

  • Performance may vary across imaging equipment

  • Relies on access to large, labeled datasets

Future work should focus on:

• Lightweight, adaptive models for low-resource settings

• Cross-domain testing

• Enhanced explainability

• Reducing dataset dependency

Conclusion

Detecting tuberculosis through deep learning has shown great promise in improving diagnosis, especially in areas where medical resources are limited. The integration of advanced models like CNNs and transformers has led to better exactness and potential to visualize decision areas using heatmaps. These improvements not only make the system more effective but also help build trust by making the results easier to interpret. Techniques like data augmentation and feature optimization have further improved the model\'s ability to handle diverse medical images.Still, there are some challenges that need attention. The requirement for large labeled datasets, high processing power, and inconsistent image quality across sources can affect model performance. Despite these issues, the findings indicate that AI-powered tools can become valuable aids for doctors in diagnosing TB. Future research should aim to make these systems lighter, more transparent, and easier to apply in real-world clinics. With continuous development and support from healthcare professionals and researchers, deep learning can play a major role in reducing the burden of tuberculosis globally.

References

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Copyright

Copyright © 2025 Hemalatha K, Niteesh Kumar , Sangmesh , Srujan Kumar D, Sudarshan Rudrapure. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.