Comparative Analysis of Backbone Networks for Deep Knee MRI Classification Models

This paper focuses on different types of backbone networks for machine learning architectures which perform classification of knee Magnetic Resonance Imaging (MRI) images. This paper aims to compare different types of feature extraction networks for the same classification task, in terms of accuracy and performance. Multiple variations of machine learning models were trained based on the MRNet architecture, choosing AlexNet, ResNet, VGG-11, VGG-16, and Efficientnet as the backbone. The models were evaluated on the MRNet validation dataset, computing Area Under the Receiver Operating Characteristics Curve (ROC-AUC), accuracy, f1 score, and Cohen’s Kappa as evaluation metrics. The MRNet-VGG16 model variant shows the best results for Anterior Cruciate Ligament (ACL) tear detection. For general abnormality detection, MRNet-VGG16 is dominated by MRNet-Resnet in confidence between 0.5 and 0.75 and by MRNet-VGG11 for confidence more than 0.8. Due to the non-uniform nature of backbone network performance on different MRI planes, it is advisable to use an LR ensemble of: VGG16 on a coronal plane for all classification tasks; on an axial plane for abnormality and ACL tear detection; Alexnet on a sagittal plane for abnormality detection, and an axial plane for meniscal tear detection; and VGG11 on a sagittal plane for ACL tear detection. The results also indicate that the Cohen’s Kappa metric is valuable in model evaluation for the MRNet dataset, as it provides deeper insights on classification decisions.