Deep learning MRI analysis for automated knee injury diagnosis

Abstract

Magnetic Resonance Imaging (MRI) plays a pivotal role in non-invasively diagnosing knee injuries. This research focuses on cost-effective, efficient solutions for enhancing automated knee injury detection in MRI scans. The study aims to boost diagnostic accuracy for abnormalities, Anterior Cruciate Ligament (ACL) tears, and meniscal tears using advanced deep learning techniques. Transfer learning is employed, combining pretrained neural networks with transfer models. AlexNet and SqueezeNet are explored as feature extraction architectures, assessing the attention mechanism and maxpooling for sequence reduction. This yields four models, MRNet, MRNet-Squeeze, MRNet-Attend, and MRNet- SqueezeAttend. The primary evaluation metric is the Area Under the ROC Curve (AUC), providing a comprehensive assessment by averaging AUC scores for abnormal, ACL tear, and meniscus tear labels. The initial MRNet achieves the highest AUC (0.940) for anomaly detection, while MRNet-Squeeze excels in diagnosing ACL damage. MRNet- SqueezeAttend achieves the highest AUC (0.885) for meniscus tears. The ensemble of all four models outperforms individual models with an outstanding average AUC of 0.931. Each model exhibits unique strengths. MRNet excels at spotting anomalies, MRNet-Squeeze accurately detects ACL tears, and MRNet-SqueezeAttend excels in identifying meniscal tears. Notably, the ensemble leverages these diverse strengths to deliver cutting-edge results for all injury types. Further investigation reveals varying correlations between model-specific predictions and different diagnosis or sequence combinations. Scrutinizing the MRI sequence frames capturing the models' attention identifies key contributors to the diagnosis.