What it does
SC2-Net uses a Convolutional Neural Network to predict a possible disease like Covid-19 from a chest x-ray image. It was fed 2k+ images to train on and is able to make predictions with 70-80% accuracy on most new samples. I also developed a web user interface that allows you to upload an image and requests a prediction from the model. The prediction result is then visualized.
Challenges I ran into
It is really hard to build a usable chest x-ray dataset with Covid-19 samples to train the model and get good generalized results. The main problem is the distinction between normal pneumonia and Covid-19. My first model was really bad at predicting with approx. 20% accuracy, but I was able to improve it over time.
What I learned
This was my first time working with Convolutional Neural Networks, so I had to research and learn a lot about them. That helped me learn something new and interesting about this type of network and how to use them efficiently. I also learned a lot about creating large image datasets for the network.
My project is similar to another existing solution: https://github.com/lindawangg/COVID-Net
What's next for SC2-Net / Covid-19 X-Ray Detector
The model is still overfitting and needs more data for training and validation. It can also be improved by tweaking some parameters to make predictions more accurate.
Try It out
amazon-web-services, angular.js, keras, netlify, python, tensorflow