Machine Learning for Breath-Based Cancer Diagnosis

Non-invasive breath analysis has substantial potential for monitoring of a wide range of medical conditions and observation of overall health status. Breath testing is easy and painless; it can be done quickly and inexpensively, and can be repeated as often as needed, making it an attractive approach for screening or clinical diagnosis. In this work, we aim to leverage machine-learning principles to improve and validate the performance of a novel breath-based cancer screening tool.

Intern: 
Robyn Larracy
Faculty Supervisor: 
Erik Scheme
Province: 
New Brunswick
Partner: 
Partner University: 
Program: