Introduction

Figure 1

Chest X-ray diseases

Visualisation

Figure 1

Example X-rays
Example X-rays

Figure 2

RGB image
RGB image

Figure 3

Example greyscale numpy array
Example greyscale numpy array

Figure 4

Example greyscale image
Example greyscale image

Figure 5

Final example image
Final example image

Figure 6

Accessing a single value

Figure 7

Accessing a submatrix

Figure 8

Accessing strided rows and columns

Figure 9

Accessing a row

Figure 10

Accessing multiple rows

Figure 11

Accessing multiple columns

Figure 12

Accessing strided columns

Data preparation

Figure 1

X-ray augmented
X-ray augmented

Neural networks

Figure 1

An example neuron receiving input from 3 other neurons

Figure 2

{a;t=“A simple neural network with input, output, and 2 hidden layers”}

Figure 3

Plots of the Sigmoid, Tanh, ReLU, and Leaky ReLU activation functions

Figure 4

2D Convolution Animation by Michael Plotke

Figure 5

Average inflammation

Figure 6

Maximum inflammation

Figure 7

Minumum inflammation

Figure 8

Average inflamation with legend

Figure 9

Average inflamation and Patient 5

Figure 10

Max Min subplot

Figure 11

Heat map

Figure 12

imagesc Heat map

Writing MATLAB Scripts

Creating Functions

Figure 1

Plotting patient vs mean

Repeating With Loops

Figure 1

This process is illustrated below: debugger-demo

Figure 2

inflammation-01.png

Figure 3

inflammation-02.png

Figure 4

inflammation-03.png

Making Choices