Training a CNN from scratch with data augmentation for melanoma detection using Keras
I will be sharing a script using Keras for training a Convolutional Neural Network (CNN) from scratch with data augmentation for melanoma detection. You can view the code from this GitHub repository. In the previous post, the CNN was trained from scratch without augmenting the data.
Before proceeding, make sure that you structure the data as follows (the numbers represent the number of images in each file):
You can download the data from, here. I used two classes as you can see from the figure above (nevus and melanoma). For training, I kept 374 images in each class to keep the data balanced.
To run the code:
$ python cnn_with_augmentation.py
The results will not be optimal, as the purpose is to show how one can train a CNN from scratch.
What variables to edit in the code?
You need to edit the following variables to point to your data:
train_directory (path to your training directory)
validation_directory (path to your training directory)
test_directory (path to your testing directory)
What should you expect (outputs)?
Training and validation accuracy
Training and validation loss
In addition to some other values (i.e. accuracy, confusion matrix) that will be displayed on the console.