| permalink | index.html |
|---|---|
| site | sandpaper::sandpaper_site |
This lesson introduces the core concepts of artificial neural networks through a practical application in healthcare: training a model to classify chest X-ray images.
You’ll learn how to:
- Load and visualize medical imaging data
- Prepare images for use in machine learning
- Build and train a convolutional neural network
- Evaluate model performance
- Explore explainability techniques using saliency maps
By the end of the lesson, you'll have constructed a neural network capable of detecting pleural effusion in chest X-rays — a real-world example of how machine learning can assist clinical decision-making.
:::::::::::::::::::::::::::::::::::::::::: prereq
You need to understand the basics of Python before tackling this lesson. The lesson sometimes references Jupyter Notebook although you can use any Python interpreter mentioned below in the setup instructions.
::::::::::::::::::::::::::::::::::::::::::::::::::
To get started, follow the setup instructions mentioned to download data and install a Python interpreter.