We wanted to build something good after our first idea failed
What it does
Given a picture of a "mole" it classifies it.
How we built it
We scrapped data from the web and used HAM10000 dataset from kaggle to build an autoML to detect "moles". We use a flask server to interact and manage the states.
Challenges we ran into
Time, had less than eight hours to build it. High network traffic, had to compromise between datasets. GCP documentation.
Accomplishments that we're proud of
Making something useful in such a short time.
What we learned
cleaning data, auto ml and GCP.
What's next for Dignaosaurious
we would like to better train the skin model and then add other easy to diagnosable diseases like heat beat murmmers and more.