Skin Cancer Classification - Convolutional Network

Overview - Active Project

This project utilizes a convolutional network to identify 9 different kinds of skin cancers including melanoma, nevus, and more. The model is trained on over 2,200 pictures of various skin cancers based off of this dataset. This model implements fundamental computer vision and classification techniques and includes a step-by-step implementation of the model as well as in-depth notes to customize the model further for higher accuracy.

Project Website

If you would like to find out more about the project, please checkout: Project Website

Installing the libraries

This project uses several important libraries such as Pandas, NumPy, Matplotlib, and more. You can install them all by running the following commands with pip:

pip install pandas
pip install numpy

python -m pip install -U matplotlib
pip install seaborn

pip install -U scikit-learn
pip install tensorflow

If you are not able to install the necessary libraries, I recommend you use Jupyter Notebook with Anaconda. I have a .ipynb file for the project as well.

Configurations

This project utilizes a CSV file for loading the dataset. If you have a CSV file full of text that you would like to use, please feel free to use this code to load your dataset in to the file:

with open("YOUR-TRAINING-DATA.p", mode = 'rb') as training_data:
    train = pickle.load(training_data)

with open("YOUR-VALIDATION-DATA.p", mode = 'rb') as validation_data:
    valid = pickle.load(validation_data)

with open("YOUR-TEST-DATA.p", mode = 'rb') as testing_data:
    test = pickle.load(testing_data)

License

MIT