Testings are essential to detect and containerize COVID-19 pandemic. However, the need for testing is excessive and the equipment is in shortage around the world. Understand the situation, we provide a solution for this rising issue.

What it does

  • COVID-19 detector tentatively predicts the probability of an infected patient based on Chest CT scan images (Computed Tomography scan technology is a popular service at most hospitals). We aim to use this project to detect people with high potential of having COVID-19. Thus, health care providers can approach, test, and deliver supports to these patients faster. The detector has a high precision rate at 91% and gives prediction in only seconds.

How we built it

  • The backend was built with Python with Flask
  • Front-end was built with HTML, CSS, javascript and Bootstrap
  • The ML model was trained using Custom Vision AI from Microsoft Azure with a set of data which consists of chest CT scan images of 329 positive COVID-19 patients and 387 negative cases.
  • The web app was deployed using Microsoft Azure Web Service with containerized by Docker

Challenges we ran into

  • We had a difficult time to find a good, reliable dataset of positive COVID-19 chest CT scan images.
  • We had problems with deploying to Azure Web server at first because we can not set up the pipeline with GitHub.
  • We also had problems with some stylings with CSS.

Accomplishments that we're proud of

  • The model that we trained has a precision rate of 91% and a recall rate of 85.6%.
  • We've successfully deployed our web app to azure web service.
  • The website has every functionality we planned to implement.

What we learned

  • How Flask serves 2 static folders
  • How to utilize the Microsoft Azure Custom Vision to quickly train ML model
  • How to deploy a web app to Azure Web Service with Docker

What's next for COVID-19

  • We look forward to improving the data set to get better predictions.
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