Acute Knee Injury Detection with Magnetic Resonance Imaging (MRI)

The anterior cruciate ligament (ACL) is a major ligament in the knee that helps to stabilize
the joint and prevent excessive forward movement of the shinbone. An ACL tear is a common injury, especially among athletes who participate in sports that involve pivoting and sudden changes
in direction. This paper proposes an ensemble model, which includes three deep learning models
(EfficientNet-B7, ResNet-152V2, and DenseNet-201) and a genetic algorithm, to detect and classify
ACL tears using knee magnetic resonance imaging (MRI). The ensemble model was trained on the
KneeMRI dataset, which comprises labeled MRI images. The deep learning models can learn to
identify subtle changes in ligament structure and signal intensity that are associated with ACL
tears and the genetic algorithm is used to find the optimal prediction. The proposed ensemble
model was evaluated using the KneeMRI dataset. The dataset was preprocessed using data augmentation techniques. Then, the ensemble model was applied to the KneeMRI dataset, evaluated,
and compared with previous models. The accuracy, recall, precision, specificity, and F1 score of our
proposed ensemble model were 99.68%, 98.73%, 99.52%, 99.62%, and 98.94%, respectively. Thus,
our ensemble model has an unrivaled perceptive outcome and could be used to accurately identify
and classify ACL tears, improving patient outcome.