Revolutionizing ACL Tear Detection Using Deep Learning Models

Abstract

Anterior cruciate ligament (ACL) injuries are considered to be one of the most widespread and acute knee disorders that can require appropriate and timely diagnosis because this can be one of the conditions that will be used to make decisions regarding the treatment. In manual inspection of magnetic resonance imaging (MRI), time-consuming mistakes are involved in interobserver errors. To address this problem, we propose a hybrid deep learning model, which will be based on EfficientNet-B0 and MobileNetV2, integrated with slice-level attention pooling and late fusion in the coronal and sagittal directions. The MRNet data were preprocessed through normalization, resizing, padding of the slice and augmentation of the data so as to enhance generalization. The sampling plan was identified as a dynamic weighted sampling technique to overcome the problem of class imbalance, whereas the training was conducted with the assistance of AdamW optimization, label smoothing, dropout, mix up augmentation, and exponential moving average (EMA) tracking. The experimental results show that the hybrid CNN was more efficient than the single architectures and the overall accuracy of the hybrid model is 94.1, the precision was 0.98, the recall was 0.89 on positive cases and the overall performance was balanced. The findings of the DenseNet169, EfficientNet-B0 and MobileNetV2 are 93, 91.6 and 90 percent which proves the outstanding of the hybrid approach. The model was also tested using precision, recall, F1-score and confusion matrices, which proved its strength. In this paper, I will give an insight into how a hybrid deep learning and attention can work to identify ACL tear on MRI and offer a reliable and scalable solution to assist radiologists and improve clinical decision-making.