CNN With Transfer Learning for Melanoma Detection Using Keras

I will be sharing a script using Keras for training a Convolutional Neural Network (CNN) with transfer learning for melanoma detection. You can find the code in 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.

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

ROC curve

In addition to some other values (i.e. accuracy, confusion matrix) that will be displayed on the console.

If you would like to train a CNN from scratch, you can see this post. If you like to train a CNN from scratch with data augmentation, you can see this post.

Leveraging machine/deep learning and image processing in medical image analysis. You can kindly find more information about me on my website:

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store