## 1 Introduction  
The knee is a hinge joint held together by four ligaments. The ligaments are a structure in the knee that hold the bones together and help control joint motion. As shown in Figure 1, there is a ligament (collateral ligament) on each side of the knee and two ligaments deep within the knee. The anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) are the two ligaments in the knee that cross each other. The two ligaments attach to the end of the thigh bone on one side and the top of the tibia on the other. The anterior gliding of the tibia over the femur is controlled by the ACL, which essentially prevents too much forward motion [1]. A certain degree of anterior motion is necessary for the knee joint, but too much activity may dama  
Figure 1: Knee joint structure. ACL injuries are prevalent in sports activities [2; 3], and their incidence of has been on the rise in the US [4]. The cost of diagnosis and treatment of ACL injuries surpasses USD 7 billion annually, directly or indirectly [5]. If left untreated, ACL injuries can cause knee instability, meniscal damage [6], and osteoarthritis [7], significantly impacting an individual's health and daily life. Raising awareness about ACL injuries and encouraging athletes to take necessary precautions is vital. Timely and accurate diagnosis in combination with effective treatment are essential for preventing further damage and improving outcomes for patients. X-ray, ultrasound, and computed tomography (CT) can usually diagnose ACL injuries, and magnetic resonance imaging (MRI) is often used in the medical field [8]. MRI has become the imaging technique of choice for evaluating the ACL because it is more accurate and sensitive for that ligament [9]. Tears in the ACL are generally rated in three grades. Grade 0 means that the ACL is intact, grade 1 is a partial tear, and grade 2 is a complete tear [10]. In recent years, with the continuous development of deep learning techniques, computers have assisted in medical diagnosis as an emerging field. Examples include the classification, detection, or segmentation of medical images. The use of computer-assisted diagnosis of the ACL has gone through two phases, including the use of traditional machine learning-assisted diagnosis and the use of neural network-assisted diagnosis. [PERSON] et al. [11] created the KneeMRI dataset and used machine learning to diagnose the severity of ACL tears. They used two feature extraction techniques, histogram of oriented gradients (HOG) and gist, and trained support vector machine (SVM) and random forest (RF) models using these features. They tested multiple hyperparameters and found that an SVM with HOG features had the best performance, achieving an 89.4% area under curve (AUC) for damage detection and a 94.3% AUC for complete tear detection. In the field of deep learning, [PERSON] et al. [12] compared various machine learning and deep learning algorithms on region of interest (ROI) data and found that SVMs and convolutional neural networks (CNN) achieved the best results. [PERSON] et al. [13] used both two-dimensional (2D) and three-dimensional (3D) CNNs to classify ACL tears, with overall accuracy rates of 89% and 92%, respectively. [PERSON] et al. [14] enhanced the interpretability and reduced the model size of a lightweight network by incorporating attention and squeeze modules, resulting in an average AUC of 98.3% on the Chiba knee dataset and 98.0% on the Stanford knee dataset. [PERSON] et al. [15] utilized spatial attention [16] integrated onto ResNet-18 to optimize knee injury detection, achieving a high AUC of 97.7% for ACL tear detection. [PERSON] et al. [17] designed a custom CNN model to classify ACL tears, achieving excellent performance with accuracy, precision, sensitivity, specificity, and F1-score metrics all above 98%. [PERSON] et al. [18] conducted a study on the influence of input field of view and dimensionality on ACL tear detection.