README.md

---
license: apache-2.0
base_model: convnext_tiny_in22k
tags:
- medical
- healthcare
- image-classification
- brain-tumor-detection
datasets:
- medical-images
language:
- en
library_name: transformers
pipeline_tag: image-classification
---

# Brain Tumor Detection

## Model Description


    This model is a ConvNeXt Tiny architecture trained with FastAI for detecting brain tumors in MRI scans.
    It can classify brain MRI images as either showing signs of a tumor or being normal (no tumor detected).
    Note: This model uses FastAI format and requires specific loading procedures.
    

## 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**: convnext_tiny_in22k
- **Classes**: 2
- **Input**: RGB images (224x224 pixels)


### Classes
- No Tumor
- Tumor Detected

## Usage

```python
from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch

# Load model and processor
model = AutoModelForImageClassification.from_pretrained("your-username/brain-tumor-detection")
processor = AutoImageProcessor.from_pretrained("your-username/brain-tumor-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.


## FastAI Usage

This model uses FastAI format. To use it:

```python
from fastai.vision.all import load_learner
import pathlib
import platform

# Fix for cross-platform compatibility
if platform.system() == 'Windows':
    pathlib.PosixPath = pathlib.WindowsPath

# Load the model
model = load_learner('model.pkl')

# Make prediction
prediction, pred_idx, probs = model.predict(image)
print(f"Prediction: {prediction}")
```

## Requirements

- fastai<2.8.0
- torch<2.7
- timm
- pathlib (for cross-platform compatibility)