Detecting Severity and Causes of Skin Burn Using Image Detection

Local Chapter Berlin, Germany Local Chapter

Coordinated byGermany ,

Vishu Kalier

Status: Completed

Project Duration: 20 May 2023 - 30 Jun 2023

Open Source resources available from this project

Github Repository

Project background.

Burns are among the most prevalent skin issues encountered in daily life, with a variety of causes such as boiling water, electricity, and UV rays. The severity of burns depends on both the victim and the source of the injury. Burns are classified based on their severity, ranging from mild cases that may present as a simple rash or boil to severe cases resulting from electric shocks that can lead to the breakdown of the skin’s epidermis and capillaries, potentially proving fatal. While first aid is often used to treat burns, more severe cases necessitate immediate medical attention.

Despite their common occurrence, burns should not be taken lightly. As the general public may not have extensive knowledge about burn severity, it is essential to develop a model that can classify the severity and predict the cause of burns. This information will empower patients to make informed decisions and seek appropriate medical care in a timely manner.

The problem.

The appropriate treatment for burns relies on accurately determining the burn’s severity and cause. Consequently, classifying burns according to these factors is essential from a medical standpoint, as it ensures patients receive the correct treatment in a timely manner.
The problem statement focuses on developing the most effective model for predicting burn severity (1st, 2nd, or 3rd degree) and source (boiling water, electricity, or UV rays). The model will supply immediate information about a patient’s burn, allowing for prompt and informed decision-making regarding their care.

Project goals.

- Create a Model that accurately detects the source and severity of the burn.- Build a website or application which can be used by any person or victim.- Making the collaborators learn new skills and advance into data science and Artificial Intelligence.- Making inexperienced people get hands-on with Neural Networks, Image Recognition, and the Medical field as well.

Project plan.

  • Week 1

    Data Collection

  • Week 2

    Data Pre-processing

  • Week 3

    Data Visualization

  • Week 4

    Feature Extraction

  • Week 5

    Model Training, Model Testing, Model Cross-validation

  • Week 6

    Web or App Development

Learning outcomes.

Medical Image Processing, Computer Vision, Image Analysis and Image Recognition, Deep Learning, Neural Networks, Project Management

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