Introduction to artificial neural networks in Python: Key Points

Introduction

Chest X-rays are widely used to identify lung, heart, and bone abnormalities.
Pleural effusion is a condition where excess fluid builds up around the lungs, visible in chest X-rays.
Large public datasets like the NIH ChestX-ray dataset enable the development of machine learning models to detect disease.
In this lesson, we will train a neural network to classify chest X-rays as either “normal” or showing pleural effusion.
We begin by loading a balanced dataset of labeled chest X-ray images.

X-ray images can be loaded and visualized using Python libraries like OpenCV and NumPy.
Images are stored as 2D arrays (grayscale) or 3D arrays (RGB).
Visual inspection helps us understand how disease features appear in imaging data.
Preprocessing steps like resizing and standardization prepare data for machine learning.

Data should be split into separate sets for training, validation, and testing to fairly evaluate model performance.
TensorFlow expects input images in the shape (batch, height, width, channels).
Data augmentation increases the variety of training data by applying random transformations.
Augmented images help reduce overfitting and improve generalization to new data.

Neural networks are composed of layers of neurons that transform inputs into outputs through learnable parameters.
Activation functions introduce non-linearity and help neural networks learn complex patterns.
Dense (fully connected) layers connect every neuron from one layer to the next and are commonly used in classification tasks.
Convolutional layers apply filters to extract spatial features from images and are the core of convolutional neural networks (CNNs).
Dropout helps reduce overfitting by randomly disabling neurons during training.

Neural networks are trained by adjusting weights to minimize a loss function using optimization algorithms like Adam.
Training is done in batches over multiple epochs to gradually improve performance.
Validation data helps detect overfitting and track generalization during training.
The best model can be selected by monitoring validation loss and saved for future use.
Final performance should be evaluated on a separate test set that the model has not seen during training.

Saliency maps visualize which parts of an image contribute most to a model’s prediction.
GradCAM++ and ScoreCAM are commonly used techniques for generating saliency maps in convolutional models.
Saliency maps can help build trust in a model, but they may not always reflect true model behavior.
Explainability methods should be interpreted cautiously and validated carefully.