Looking at individuals in developing countries and how technology is underdeveloped their, it is clear that their is a need for a quick, easy-to-use web app, that can provide quick preliminary information about the patient's condition.
What it does
A web app that allows the user to enter a photograph of an eye (fundus photography). The web app will then display results, and whether the photograph in question has Diabetic Retinopathy.
How we built it
Challenges we ran into
The biggest challenge we ran into was trying to figure out a way to communicate between the trained machine learning model, and the web app. We eventually settled on using firebase for this. Other challenges include: figuring out how to save the model, and use it on command rather than retrain it every time, as well as getting the web app to communicate with firebase effectively.
Accomplishments that we're proud of
We are most proud of the following things: a working machine learned image classifier, communicating with firebase, and getting all aspects of our project finished in the given time frame at the hackathon.
What we learned
We learned a lot about the following technology: React, Tensorflow's API (Python), Firebase
What's next for Blindness Detection
The next steps include: build a better training model (by using a larger sample database), and categorize the user's Diabetic Retinopathy into its various stages of severity.