## 4 Classification Method
### Evaluation Method  
In this study, the deep learning model was evaluated using classification performance metrics and a confusion matrix. Classification performance metrics were calculated based on four key indicators: true-positive (_TP_), false-positive (_FP_), true-negative (_TN_), and false-negative (_FN_) values. True positives refer to the number of samples correctly classified as positive; false positives refer to the number of samples incorrectly classified as positive; true negatives refer to the number of samples correctly predicted as negative; and false negatives refer to the number of positive samples incorrectly predicted as negative.  
The classification performance metrics included classification accuracy (_Acc_), _precision_, _recall_, and _F1 score_. Classification accuracy is the ratio of correctly predicted samples to the total number of samples, with higher _Acc_ indicating stronger overall model performance; _precision_ is the proportion of true positives among all positive predictions, with higher  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Optimizer & Adam \\ Kernel Size & \(7\times 7\) \\ Kernel Size (Residual Layer) & \(3\times 3\) \\ Pooling Layer & \(2\times 2\) Max Pooling \\ Mini-batch Size & 56 \\ Learning Rate & 0.0001 \\ Max Epochs & 740 \\ Activation Function & ReLu \\ \hline \hline \end{tabular}
\end{table}
Table 4: The initialization parameters of the residual network.  
Figure 10: Schematic of the residual block.  
precision_ indicating lower error rates when dealing with positive samples; _recall_ is the proportion of true positives among all actual positives, with higher _recall_ indicating the better ability of the model to correctly identify the category; \(F1\)_score_ is the harmonic mean of _recall_ and _precision_, with a higher \(F1\)_score_ indicating a better balance between coverage and recognition ability, leading to higher performance on datasets with different class sample sizes. The calculation of classification performance metrics is given by Equations (8)-(11).  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN}, \tag{8}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{9}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{10}\]  
\[F1\;Score=\frac{2\cdot(Precision\cdot Recall)}{Precision+Recall} \tag{11}\]  
Considering accuracy, the higher it is, the stronger the model's overall classification capability, i.e., the stronger it is able to correctly identify most samples. Regarding precision, the higher it is, the lower the error rate among the samples predicted as positive by the model. In terms of recall, the higher it is, the stronger the model's ability to cover and identify true-positive (_TP_) values. With the \(F1\) score, the higher it is, the better the balance the model achieves between precision and recall, indicating superior performance on datasets with varying sample sizes across different classes.  
In deep learning, a confusion matrix is a supervised learning tool used to summarize the prediction results of a classification model. It presents data in a matrix form, classifying records in the dataset based on the true and predicted categories and providing a visual analysis of the model's recognition accuracy.