  This work employs a pre-trained, multi-view Convolutional Neural Network
(CNN) with a spatial attention block to optimise knee injury detection. An
open-source Magnetic Resonance Imaging (MRI) data set with image-level labels
was leveraged for this analysis. As MRI data is acquired from three planes, we
compare our technique using data from a single-plane and multiple planes
(multi-plane). For multi-plane, we investigate various methods of fusing the
planes in the network. This analysis resulted in the novel 'MPFuseNet' network
and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior
Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977
and 0.957 respectively. We then developed an objective metric, Penalised
Localisation Accuracy (PLA), to validate the model's localisation ability. This
metric compares binary masks generated from Grad-Cam output and the
radiologist's annotations on a sample of MRIs. We also extracted explainability
features in a model-agnostic approach that were then verified as clinically
relevant by the radiologist.
