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Scaneurysm AI - Brain CT Aneurysm Detection

An AI-powered system for detecting brain aneurysms in CT scan images using PyTorch and AWS SageMaker.

Overview

This project implements a deep learning model that analyzes brain CT scans to detect potential aneurysms. It uses a VGG16-based architecture and can be deployed as a serverless endpoint on AWS SageMaker.

Prerequisites

  • Python 3.9+
  • AWS Account with SageMaker access
  • Docker (for local testing)
  • Required Python packages (see requirements.txt)

Setup

  1. Create and activate virtual environment:
./setup_venv.sh
  1. Install dependencies:
pip install -r requirements.txt
  1. Setup AWS infrastructure:
./setup_aws_infrastructure.sh

Project Structure

.
├── my_model/
│   ├── model.pth              # Your trained model weights
│   └── code/
│       ├── inference.py       # Model inference code
│       └── requirements.txt   # Inference dependencies
├── test-local.ipynb          # Local testing notebook
├── final.ipynb               # Deployment notebook
├── setup_aws_infrastructure.sh
├── setup_venv.sh
├── requirements.txt
└── Dockerfile

Model Details

  • Architecture: Modified VGG16
  • Input: Brain CT scan images (224x224 RGB)
  • Output: Binary classification (Aneurysm/Non-aneurysm)
  • Framework: PyTorch 2.1.0

Deployment

  1. Package your trained model:
# Ensure your model.pth is in the my_model/ directory
tar -czf model.tar.gz my_model/

  1. Open final.ipynb in Jupyter and follow the deployment steps.

  2. The deployment will create a serverless SageMaker endpoint.

Testing

  1. Local testing:

    • Use test-local.ipynb to verify model functionality

  2. Endpoint testing:

    • Use the provided test functions in final.ipynb
    • Test with sample CT scan images via URLs

API Usage

import boto3
import json

def invoke_endpoint(endpoint_name, image_url):
    runtime = boto3.client('sagemaker-runtime')
    response = runtime.invoke_endpoint(
        EndpointName=endpoint_name,
        ContentType='application/json',
        Body=json.dumps({"url": image_url})
    )
    return json.loads(response['Body'].read().decode())

Environment Variables

Required AWS environment variables:

  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY
  • AWS_DEFAULT_REGION

Contributing

  1. Fork the repository
  2. Create your feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a new Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • VGG16 architecture from torchvision
  • AWS SageMaker team for serverless inference capabilities

Important Note

Remember to bring your own trained model (model.pth). This repository does not include pretrained weights.