Veterinary Disease Detection Using Machine Learning and Deep Learning Architectures

In recent advancements, researchers are leveraging deep learning and machine learning for improving medical care, particularly within the veterinary field. The development of smart, wearable biosensing devices, equipped with non-invasive sensors and integrated with machine learning algorithms, facilitates real-time health monitoring. Continuous monitoring of health data through these devices offers valuable insights into adverse health events, such as seizure and syncope patterns, allowing for early detection of subtle physiological changes. This leads to timely veterinary interventions and personalized care, significantly enhancing the quality of life for canines. This research aims to explore the development of innovative deep models that can reliably recognize adverse health conditions in canines based on the collected physiological data to provide timely and accurate interventions when necessary.

Faculty Supervisor:

Andrew Winterborn

Student:

Partner:

NerveX Neurotechnologies, Inc.

Discipline:

Engineering

Sector:

Artificial Intelligence

University:

Queen's University

Program: