1 million people every year die from malaria 90% of which is in Saharan Africa. Many of these people have inadequate number of doctors to analyze their patients for malaria. Here in Project Proboscis we want to make a change so these people get adequate help.
What it does
Classifies the difference between cells uninfected and parasatized with malaria using Tensorflow. Which is trained by 27,000 images.
How we built it
First we preprocessed the image data, to make it suitable to use for Machine Learning. Then, we used a Convlutional Neural Network to build the AI model,and used that to make the predictions.
Challenges we ran into
We weren't able to figure out how to use the Convolutional Neural Network to predict when given an image, but we figured it out after looking through the internet to find how predictions work.
Accomplishments that we're proud of
We combined server side processing with ML code to make a fully functional classifier, that can also be easily used by other people.
What we learned
We learned about Convolutional Neural Networks, and how to use a model for predicting a specific image. We also learned a lot about server side processing, and tools that we can use such as Heroku, that make it much easier to make WebApps like this.
What's next for Proboscis
Testing Proboscis AI and seeing what the difference can make in malaria infested countries.