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  Home page

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  Skin burn classifier

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  First degree burn

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  Second degree burn

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  Third degree burn

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  Nearby hospitals

Burn Out

💡 Inspiration

Correct evaluation of different degrees of burn is a bit challenging in the visual assessment method. The accuracy can be improved using imaging methods such as Laser Doppler Imaging, but it could be very expensive and not available at every clinic/hospital. Skin burns in color images must be accurately detected and classified according to burn degree in order to assist clinicians during diagnosis and early treatment. Especially in emergency cases in which clinical experience might not be available to conduct a thorough examination with high accuracy, an automated assessment may benefit patient outcomes. Therefore to combat this issue, we came up with an idea for creating a web application where you just need to upload a picture of the area with burnt skin and get the information about the degree of burn. You can also get various first aid methods for the burn. This solution is very cost-effective and using ML will increase the correct evaluation of the burn.

💻 What it does

Our web app "Burn Out" is built with features:

• Upload a picture of the area with burnt skin and get the information about the degree of burn.
• Get the information about first aid for burn.
• Get the location of the nearest hospitals.

⚙️ How we built it

• Frontend: React Js
• Backend: Node Js
• Tensorflow.Js: For ML and disease prediction
• Google Teachable Machine: For training the model
• Styling: Tailwind CSS

🧠 Challenges we ran into

• Completing the project was a challenge because we had to implement the project by a deadline.
• It was a bit difficult to train the model because of the limited dataset.
• It was difficult to get the image from the user and then process it.

🏅 Accomplishments that we're proud of

• We are proud of the fact that we were able to complete the project in the given time.
• We are proud of the fact that we were able to train the model and deploy it.

📖 What we learned

• How to use Google teachable machine to predict the disease.
• How to use Tensorflow.js to build the ML model.

🚀 What's next for Burn Out

• Building a mobile app
• Deploying the web app.
• Improving the accuracy of the model.

Built With

• google-techable-machine
• node.js
• react
• tailwind-css
• tensorflow.js