Inspiration

We want to make skin cancer diagnosis available to everyone in the world, especially people in third world countries who don't have access to doctors and dermatologists.

Alt Graph #1

Figure 1: This statistical visualization of diagnosis age for malignant tumours stood out to us, and was one of the reasons we decided it was a problem that needed to be diagnosed early on.

What it does and how we built it

e-Diagnose was trained on a dataset of labelled malignant and benign skin cancer tumours. It was retrained with Google's TensorFlow Inception model.

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Challenges we ran into

Understanding the mathematical and theoretical aspects of how neural networks work. We had troubles grasping the need to use a docker container, but quickly we realized it was very effective.

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Accomplishments that we're proud of

We're proud of achieving a similar classification accuracy to real-world skin cancer diagnosis.

What's next for e-Diagnose

We plan to implement our algorithm and training model on a front-end system, by creating a cross-platform mobile application that will let anyone take a picture of a suspected tumour.

Installation and Project Setup

1. Install Docker Toolbox for your machine.

2. Clone the repository.

git clone https://github.com/paarthmadan/skin-cancer-cnn

3. Use the trained model, or retrain the model by running retrain.py

4. Run main.py

python main.py

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