Anterior cruciate ligament tear detection based on convolutional neural network and generative adversarial neural network

Knee ligament tear injury is frequent in many volleyball, football, basketball, and cricket players. In the past, various deep learning-based ACL tear detection schemes using knee magnetic resonance imaging (MRI) have been presented. It has shown challenges in ACL tear detection performance due to class imbalance issue arising due to uneven training samples and poor feature representation. This paper presents a simple and effective ACL knee ligament tear detection method with a convolutional neural network (ATD-CNN) to lessen the intricacy of the network. Further, the self-attention mechanism is used to improve the feature representation of MRI image information, neglect the irrelevant information in deep features, and enhance the classification accuracy. To diminish the class imbalance issue, generative adversarial network (GAN) is used to construct the synthetic database. The performance of the ATD-CNN with self-attention is assessed on the MRNet database using precision, accuracy, F1-score, and recall. ATD-CNN provides an accuracy of 90.10% for the original and 93.93% and augmented datasets. However, the ATD-CNN without attention mechanism resulted in 89.60% and 92.30% accuracy for original and augmented dataset. The proposed ATD-CNN model indicates that it can be utilized to detect ACL tears automatically and outperforms the existing schemes for tear detection.