README.md

metadata

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

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.