## 4 Experiment and Analysis
### Results and Analysis
#### 4.3.2 Compare Experiment  
In the 2012 ILSVRC Challenge, AlexNet won the championship and was well ahead of second place. This led to extensive research on AlexNet, and led to the belief that \"the deeper the network, the higher the accuracy.\" As VGGNet, Inception v1, Inception v2, and Inception v3 have been continuously verified and strengthened, this belief is increasingly recognized. However, as the number of layers of the network increases, the loss in the training dataset rises, which causes the degradation problem of neural networks. To solve the degradation problem, [PERSON] [22] proposed the residual neural network (ResNet), which consists of residual blocks. The structure of the residual block is shown in Figure 3.  
In order to evaluate the classification performance of the proposed loss function, we compare the CEFL loss, CE loss, and FL loss used in various ResNet classification models. The experimental results are shown from Tables 4-6. As shown from Tables 4-6, the proposed CEFL loss without using the weight function obtains better classification performance than CE loss and FL loss, which can be improved by about 1% to 5%. As the number of network layers deepens, the F1 score of CEFL loss increases 1.7% from ResNet-18 to ResNet-34, then decreases by about 1.2% from ResNet-34 to ResNet-50. Finally, the performance of ResNet-50 is increased by 5.2% over ResNet-101. However, the classification performance of models that use the re-weighted CEFL loss function is further improved. In particular, when the parameter \(\gamma=1,\lambda=4\), the model achieves the best performance on F1 score, with 88.63%. Figure 3: Residual learning: a building block. In order to verify the effectiveness of the improved CBAM module, this section introduces the improved CBAM module, traditional CBAM module, and the single-domain attention module to the CNN model for comparative experiments. The experimental results are shown from Tables 7-9. From the results presented above, it can be observed that the various ResNet models introduced to the improved CBAM module increase significantly in their PR, RC, and F1 score. This is because the CBAM module improves with the redefined imbalance degree, and can extract features that are more conducive to sample classification. It makes the model pay more comprehensive attention to the characteristics of minority samples, enhance classification accuracy, and thus, improves the classification effect of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss Function Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline CE Loss & 71.12\% & 72.65\% & 76.84\% & 75.93\% \\ FL (\(\alpha=0.25\)\(\gamma=2\)) & 75.61\% & 74.41\% & 77.93\% & 77.61\% \\ FL (\(\alpha=0.75\)\(\gamma=2\)) & 72.86\% & 72.96\% & 73.42\% & 77.25\% \\ FL (\(\alpha=1\)\(\gamma=2\)) & 73.24\% & 73.54\% & 74.89\% & 75.86\% \\ CEFL (\(\gamma=1\)) & 74.91\% & 79.22\% & 72.68\% & 81.85\% \\ CEFL (\(\gamma=2\)) & 77.23\% & 80.45\% & 75.53\% & 83.26\% \\ CEFL (\(\gamma=5\)) & 76.81\% & 78.56\% & 77.28\% & 82.50\% \\ CEFL (\(\gamma=1\)\(\lambda=4\)) & 88.63\% & 87.52\% & 86.29\% & 88.47\% \\ CEFL (\(\gamma=2\)\(\lambda=4\)) & 87.74\% & 87.96\% & 84.43\% & 89.18\% \\ CEFL (\(\gamma=5\)\(\lambda=4\)) & 87.59\% & 88.27\% & 86.17\% & 88.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F1-score based on different loss functions of various ResNets.