Spaces:
Configuration error
Configuration error
File size: 3,170 Bytes
3bf5d0a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- medical
- healthcare
- image-classification
- chest-x-ray-pneumonia-detection
datasets:
- medical-images
language:
- en
library_name: transformers
pipeline_tag: image-classification
---
# Chest X-ray Pneumonia Detection
## Model Description
This model is a fine-tuned Vision Transformer (ViT) for detecting pneumonia in chest X-ray images.
It can classify chest X-rays as either NORMAL (healthy) or PNEUMONIA (showing signs of pneumonia infection).
## Intended Uses & Limitations
⚠️ **Important**: This model is for research and educational purposes only. It should **NOT** be used for actual medical diagnosis without proper clinical validation and oversight by qualified medical professionals.
### Intended Uses
- Research and development in medical AI
- Educational purposes for learning about medical image classification
- Proof-of-concept applications with proper disclaimers
- Academic studies and benchmarking
### Limitations
- Not clinically validated
- Should not replace professional medical diagnosis
- May have biases based on training data
- Performance may vary on different populations or imaging conditions
## Model Details
- **Model Type**: Image Classification
- **Architecture**: google/vit-base-patch16-224-in21k
- **Classes**: 2
- **Input**: RGB images (224x224 pixels)
- **Accuracy**: 95.83%
### Classes
- NORMAL
- PNEUMONIA
## Usage
```python
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch
# Load model and processor
model = AutoModelForImageClassification.from_pretrained("your-username/chest-x-ray-pneumonia-detection")
processor = AutoImageProcessor.from_pretrained("your-username/chest-x-ray-pneumonia-detection")
# Load and process image
image = Image.open("path_to_image.jpg")
inputs = processor(image, return_tensors="pt")
# Make prediction
with torch.no_grad():
outputs = model(**inputs)
predicted_class_id = outputs.logits.argmax().item()
predicted_class = model.config.id2label[predicted_class_id]
print(f"Predicted class: {predicted_class}")
```
## Training Details
This model was fine-tuned from pre-trained vision transformers on medical image datasets. For detailed training information, please refer to the original model documentation.
## Evaluation
The model has been tested on held-out validation sets with the reported accuracy metrics. However, clinical evaluation and validation are required before any medical application.
## Ethical Considerations
- Medical AI models can have significant impact on human health
- Proper validation and regulatory approval required for clinical use
- Potential for bias in training data and model predictions
- Should be used responsibly with appropriate medical oversight
## Contact
For questions about this model, please create an issue in the repository.
## Citation
If you use this model in your research, please cite appropriately and acknowledge that it's for research purposes only.
|