With COVID-19 tests being carefully rationed out and there being multiple scarcities, patients may not have access to a traditional test. Our software can diagnose a patient purely on a CT scan, eliminating the need for single use tests. We used the COVID-19 Lung CT Scans by LuisBlanche on Kaggle.

What it does

Our web app has a form for submitting patient data and uploading a CT scan image. We then pass the pixel data to our server, which runs several Tensorflow models. We then take the average confidence of all the models, and return the prediction to the browser. You can test it with the CT Scan images in the Devpost Gallery.

How I built it

We built 8 Deep Learning Models with Tensorflow and Keras that integrate convolutional neural network architecture and was trained using K-fold Cross-validation, in order to make best use of a limited dataset. Our model achieved nearly 90% accuracy, allowing hospitals to use this as a tool to diagnose patients when resources are limited.

Challenges I ran into

Over the course of this hackathon we were able to create a data model which achieves nearly 90% accuracy, one issue we had was not having a powerful enough processing unit to train the model from the start of the competition. We started using a NVIDIA V100 GPU to train the model on Google Cloud Platform. Given a better processing unit from the start and more time we would've been able to achieve greater accuracy, however we still manage ~90%.

Accomplishments that I'm proud of

We used a NVIDIA V100 Graphics Processing Unit on Google Cloud Platform in order to train our models. We were also able to finish this entire project in 24 hours.

What I learned

During the course of this hackathon we were able to learn and use Django to correctly link up the website wherein a user has to upload a CT Scan to the back-end data model which can predict whether a patient has Covid-19 or not.

What's next for No Test No Problem

We plan to add a database structure to hold patient and prediction data. We hope that this functionality will make our app more appealing to healthcare professionals.

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