Data analysis

• Description: Cheetah Melanoma Detection is a solution to analyse skin lesions and assign a priority score based on the three most probable skin lesions detected by the model in order to schedule an appointment with the specialist.
• Data Source: HAM10000 dataset from ISIC archive
• Type of analysis: Deep Learning binary and multiclass models.

Startup the project

The initial setup.

Create virtualenv and install the project:

sudo apt-get install virtualenv python-pip python-dev
deactivate; virtualenv ~/venv ; source ~/venv/bin/activate ;\
    pip install pip -U; pip install -r requirements.txt

Unittest test:

make clean install test

Check for ham10k-wagon in gitlab.com/{group}. If your project is not set please add it:

• Create a new project on gitlab.com/{group}/ham10k-wagon
• Then populate it:

##   e.g. if group is "{group}" and project_name is "ham10k-wagon"
git remote add origin [email protected]:{group}/ham10k-wagon.git
git push -u origin master
git push -u origin --tags

Functionnal test with a script:

cd
mkdir tmp
cd tmp
ham10k-wagon-run

Install

Go to https://github.com/{group}/ham10k-wagon to see the project, manage issues, setup you ssh public key, ...

Create a python3 virtualenv and activate it:

sudo apt-get install virtualenv python-pip python-dev
deactivate; virtualenv -ppython3 ~/venv ; source ~/venv/bin/activate

Clone the project and install it:

git clone [email protected]:{group}/ham10k-wagon.git
cd ham10k-wagon
pip install -r requirements.txt
make clean install test                # install and test

Functionnal test with a script:

cd
mkdir tmp
cd tmp
ham10k-wagon-run