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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- medical |
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- chest-xray |
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- tuberculosis |
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- vision-transformer |
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- pytorch |
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- image-classification |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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pipeline_tag: image-classification |
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--- |
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# Tuberculosis Detection ViT Model |
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This repository contains a Vision Transformer (ViT) model for classifying chest X-ray images as **Normal** or **Tuberculosis**. |
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## Model Details |
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- **Model Type**: Vision Transformer (ViT) |
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- **Framework**: PyTorch 2.0.1 |
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- **Architecture**: |
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- Patch size: 16 |
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- Number of patches: 196 |
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- Projection dimension: 512 |
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- Number of attention heads: 8 |
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- Number of encoder blocks: 6 |
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- **Input Size**: 224x224 RGB images |
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- **Classes**: Normal, Tuberculosis |
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## Dataset |
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- **Source**: TB Chest Radiography Database |
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- **Preprocessing**: Grayscale conversion, CLAHE enhancement, Gaussian blur, RGB conversion, standardization |
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- **Training**: Early stopping with validation loss monitoring |
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## Usage |
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### Quick Start |
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```python |
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from huggingface_hub import hf_hub_download |
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import torch |
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import cv2 |
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import numpy as np |
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# Download the handler |
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handler_path = hf_hub_download(repo_id="sukhmani1303/tuberculosis-vit-model", filename="handler.py") |
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# Import and use |
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exec(open(handler_path).read()) |
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classifier = TBClassifier() |
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# Load and predict |
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image = cv2.imread("path/to/chest_xray.jpg") |
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result = classifier.predict(image) |
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print(f"Prediction: {result['prediction']}, Confidence: {result['confidence']:.4f}") |
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``` |
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### Loading the PyTorch Model |
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```python |
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from huggingface_hub import hf_hub_download |
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import torch |
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import json |
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# Download model files |
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config_path = hf_hub_download(repo_id="sukhmani1303/tuberculosis-vit-model", filename="config.json") |
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model_path = hf_hub_download(repo_id="sukhmani1303/tuberculosis-vit-model", filename="pytorch_model.bin") |
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# Load configuration |
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with open(config_path, 'r') as f: |
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config = json.load(f) |
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# Initialize your ViT model class with config |
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# model = ViT(**config) # You'll need to have your ViT class available |
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# model.load_state_dict(torch.load(model_path, map_location='cpu')) |
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# model.eval() |
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``` |
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## Performance |
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- **Training**: Early stopping with validation monitoring |
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- **Evaluation**: Tested on held-out validation set |
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- **Metrics**: Accuracy, Precision, Recall, F1-score available upon request |
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## Preprocessing Pipeline |
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1. Convert RGB to grayscale |
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2. Apply CLAHE (Contrast Limited Adaptive Histogram Equalization) |
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3. Apply Gaussian blur (5x5 kernel) |
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4. Resize to 224x224 |
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5. Convert back to RGB format |
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6. Normalize with z-score standardization |
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## Medical Disclaimer |
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⚠️ **IMPORTANT**: This model is for educational and research purposes only. It should not be used for actual medical diagnosis. Always consult qualified healthcare professionals for medical advice and diagnosis. |
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## Citation |
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If you use this model in your research, please cite: |
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``` |
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@misc{tuberculosis-vit-model, |
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author = {Sukhmani}, |
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title = {Tuberculosis Detection using Vision Transformer}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/sukhmani1303/tuberculosis-vit-model} |
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} |
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``` |
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## License |
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Apache 2.0 |
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## Contact |
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For questions or issues, please open an issue in the repository. |
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