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----
-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)