Detecting Severity and Causes of Skin Burn Using Image Detection
Challenge 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.
Goal of the Project
- 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 Timeline
Data Collection
Data Pre-processing
Data Visualization
Feature Extraction
Model Training, Model Testing, Model Cross-validation
Web or App Development
What you'll learn
Medical Image Processing, Computer Vision, Image Analysis and Image Recognition, Deep Learning, Neural Networks, Project Management
First Omdena Local Chapter Project?
Beginner-friendly, but also welcomes experts
Education-focused
Duration: 4 to 8 weeks
Open-source
Your Benefits
Address a significant real-world problem with your skills
Build your project portfolio
Access paid projects (as an Omdena Top Talent)
Get hired at top organizations
Requirements
Good English
Suitable for AI/ Data Science beginners but also more senior collaborators
Learning mindset
Application Form
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