Abstract:
One of the most common injuries incurred through physical activity is the anterior
cruciate ligament tear or ACL tear. ACL injuries tend to have a low self-recovery
rate and as such, they usually require surgery in order to reconstruct or repair the
torn ligament. The study aims to improve upon the already established methods
when trying to diagnose and classify potential ACL injuries through the use of
convolutional neural networks (CNNs) and transfer learning. Different parameters
are tested to find the optimum and best-performing model based on specific
performance metrics. A web-based decision support tool for assessing and diagnosing
ACL injury utilizing knee MRIs integrating the best performing CNN model
is developed serving as a valuable decision support tool in different healthcare
applications.
