Using AI to Help First Responders Assess Skin Burns

Burns are a common type of skin injury that cause numerous deaths around the world every year. Timely assessment of burns plays an important role in a successful treatment. Traditionally, burns are assessed through visual and tactile observation by clinicians. This method of assessment is highly inconsistent as it depends on the
availability of a clinician and the clinician’s level of experience. Deep convolutional neural networks (CNNs) have the potential to offer an alternative for burn evaluation that is accurate, fast, inexpensive, and can be performed easily by the first-responders. The objective of this research is to build a widely accessible deep CNN model for
burn assessment that takes advantage of an efficient architecture, suitable for small burn image datasets and resource-constrained devices, and also an integrated saliency mapper for accurate localization and measurement of burn areas.

Faculty Supervisor:

Peter Liu

Student:

Partner:

Skinopathy Inc.

Discipline:

Engineering

Sector:

Artificial Intelligence; Health and Related Sciences & Technology; Technology

University:

Carleton University

Program: