## 5 Discussion  
In this paper, based on the triple classification problem of an ACL tear, the channel correction module is used to address the complex training problem caused by the different MRI image distributions of different patients. Furthermore, the ordinal loss function is used to address the class imbalance problem with the grade classification of ACL tears. A generic framework is proposed in conjunction with the spatial attention mechanism. The best result of 83.3% accuracy is achieved using ResNet-18 as the backbone network, which is a 6.65% improvement compared to the baseline. The improvement over the baseline is also observed on other backbone networks.  
In Table 2, various combination schemes of the three modules are also validated, proving the effectiveness of all modules. The proposed framework is tested in several well-known backbone networks in addition to ResNet-18. The proposed framework improves accuracy on these backbone networks, as shown in Table 1.  
Deep learning studies of MRI image classification tasks based on ACL tears are common. [PERSON] et al. [11] created the KneeMRI dataset. [PERSON] et al. [12] determined that SVM and CNN yield superior results in analyzing ROI data. [PERSON] et al. [43] proposed the idea of integrating all sliced information of individuals. The research conducted by them and others has been highly beneficial for further studies. However, only a few analyses have classified all three tear grades in the human unit. For example, [PERSON] et al. [17] achieved a good AUC and accuracy for ACL tear classification, but their method only used single slices as the unit of analysis. This limitation is not ideal for clinical applications. [PERSON]'s [18] experiments demonstrated the importance of cropped slices and 3D inputs for achieving high algorithmic performance in ACL tear classification. However, their study only classified ACL tears into normal and complete tear categories, requiring manual judgment for partially torn samples, which limits its the potential for intelligent ACL tear diagnosis. A human unit is more consistent with clinical applications in practical terms, and performing the triple classification task can accelerate the diagnosis. Therefore, we propose a network framework for such a problem.  
The study in this paper still has the following limitations, and some directions for improvement are shown below:  
1. In this paper, a simple function is used to work on the channel correction, but the function's parameter is set according to human experience. Therefore, using the algorithm to obtain the hyperparameters and design a function more suitable for this task can be considered.
2. Ordinal loss function is used as the loss function for classification, and the initial weight matrix is set according to human experience. A more reasonable heuristic method can be considered to obtain suitable initial weights for the data.
3. The method proposed in this paper is applied to a triple classification task for ACL tears. In addition, the modules are applicable to many datasets. Channel correction, for instance, can be used in datasets with different image distributions, the ordinal loss can be used in datasets with orderliness between them, and so on, which merits further discussion.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & **Loss** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1** \\ \hline \multirow{2}{*}{ResNet-18} & Cross-entropy Loss & 0.767 & 0.499 & 0.483 & 0.821 & 0.480 \\  & Ordinal Loss & 0.768 & 0.552 & 0.519 & 0.835 & 0.508 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics before and after replacing the loss function.