Machine learning has many applications in healthcare. We thought it would be beneficial to combine our AI and computer science knowledge to create something that could help healthcare professionals and save lives.
There are over 1 million hospitalizations due to pneumonia in the US every year, and over 50,000 lives are lost each year. Our project could help healthcare professionals detect and treat pneumonia faster.
What it does
We have a basic UI where people can input an Xray image to the web app. The image then gets passed through the neural network, which then outputs a value of 0 (normal) or 1 (pneumonia detected).
How we built it
Challenges we ran into
We ran into a lot of trouble with the neural network. Our training data included significantly more pneumonia images than normal images, which affected our accuracy. We weren't able to solve this problem, but given more time, we would add a weight to our optimizer.
Accomplishments that we're proud of
We're proud that the neural network was able to detect pneumonia fairly accurately.
What we learned
We had never used Flask before, so we learned how to implement a successful web application using the framework. We're also relatively new to Pytorch and machine learning in general, so there was a lot of trial and error involved.
What's next for Pneumonitor
In the future, we are looking to add a multi-file upload feature that will allow doctors or other health care professionals to use our web app with multiple x-ray images. We also hope to experiment with different architectures to achieve higher accuracies.