MSc

A comprehensive study and comparison of transfer learning techniques applied to binary classification of skin lesions.

Environment

We use Miniconda to manage a virtual Python 3.6 environment and dependencies.

Install Miniconda and create the virtual Python environment with all necessary dependencies with:

./conda.sh

Activate and enter the environment at any point with:

conda activate msc

Data

Download the official ISIC2018 train set, preprocess it, and split it into our own internal train and test sets:

./isic2018.sh

Train

Run the prepared scripts to train the VGG16 transfer learning models and the custom CNN end-to-end learning models:

./train_vgg16.sh
./train_custom1.sh
./train_custom2.sh

Test

Run the prepared scripts to test the VGG16 transfer learning models and the custom CNN end-to-end learning models:

./test_vgg16.sh
./test_custom.sh

Then plot results accordingly:

./plot_vgg16.sh
./plot_custom.sh

Dissertation

The LaTeX document in doc/ is the dissertation for the MSc thesis which tells the story of the experiments.

cd doc
make