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For this project, I set out to implement three different image classifiers and fit all of them with greyscale and RGB images to observe their differences in performance and find the one that works best. Additionally, I wanted to investigate the effect of the image colour spectrum on a range of classifiers. The classifiers that I set out to imple…

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kasiotis/Skin-Cancer-MNIST-HAM10000

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Welcome to my project !!

In the same directory you can see the files where I have implemented all of my Python code for the skin cancer classification project. To run these scripts you first need to set up a few things.

  1. You need to go to the cancer-data folder and follow the instructions for importing the data.
  2. You need to set up a Python interpreter, in case you are using a virtual environment I have prepared a folder for you called venv.
  3. Once you have set up your interpreter you need to install all of the libraries that I have used in this project. Unfortunately I could not submit my virtual environment with all my pre-installed libraries because the size was too big.
  4. The jupyter notebooks with the literate programming for the detailed report, summary and abstract are found in the b8035526-ml-notebooks folder in both .ipynb and pdf formats.

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For this project, I set out to implement three different image classifiers and fit all of them with greyscale and RGB images to observe their differences in performance and find the one that works best. Additionally, I wanted to investigate the effect of the image colour spectrum on a range of classifiers. The classifiers that I set out to imple…

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