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.

Faculty Supervisor:

Erik Scheme

Student:

Robyn Larracy

Partner:

Picomole Inc

Discipline:

Engineering - computer / electrical

Sector:

Health care and social assistance

University:

University of New Brunswick

Program:

Accelerate

Current openings

Find the perfect opportunity to put your academic skills and knowledge into practice!

Government

International

Load More

International

Load More