## 4 Experiment and Analysis
### Results and Analysis
#### 4.3.2 Compare Experiment
\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 & 69.95\% & 73.71\% & 79.82\% & 81.34\% \\ FL (\(\alpha=0.25\)\(\gamma=2\)) & 78.42\% & 82.52\% & 84.65\% & 85.35\% \\ FL (\(\alpha=0.75\)\(\gamma=2\)) & 76.04\% & 77.23\% & 81.53\% & 83.79\% \\ FL (\(\alpha=1\)\(\gamma=2\)) & 75.18\% & 74.26\% & 81.81\% & 84.92\% \\ CEFL (\(\gamma=1\)) & 82.17\% & 78.82\% & 85.35\% & 88.50\% \\ CEFL (\(\gamma=2\)) & 81.86\% & 77.74\% & 85.73\% & 88.63\% \\ CEFL (\(\gamma=5\)) & 82.39\% & 79.91\% & 86.49\% & 89.14\% \\ CEFL (\(\gamma=1\)\(\lambda=4\)) & 87.42\% & 89.91\% & 90.25\% & 92.70\% \\ CEFL (\(\gamma=2\)\(\lambda=4\)) & 87.65\% & 87.56\% & 89.12\% & 93.77\% \\ CEFL (\(\gamma=5\)\(\lambda=4\)) & 88.07\% & 87.77\% & 91.05\% & 92.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy rates based on different loss functions of various ResNets. \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 & 72.33\% & 71.62\% & 74.07\% & 71.19\% \\ FL (\(\alpha=0.25\)\(\gamma=2\)) & 72.99\% & 67.75\% & 72.20\% & 71.16\% \\ FL (\(\alpha=0.75\)\(\gamma=2\)) & 69.94\% & 69.14\% & 66.78\% & 71.66\% \\ FL (\(\alpha=1\)\(\gamma=2\)) & 71.40\% & 72.83\% & 69.05\% & 68.55\% \\ CEFL (\(\gamma=1\)) & 68.83\% & 79.62\% & 63.29\% & 76.13\% \\ CEFL (\(\gamma=2\)) & 73.10\% & 83.36\% & 67.50\% & 78.50\% \\ CEFL (\(\gamma=5\)) & 71.94\% & 77.25\% & 69.84\% & 76.78\% \\ CEFL (\(\gamma=1\)\(\lambda=4\)) & 89.87\% & 85.25\% & 82.66\% & 84.61\% \\ CEFL (\(\gamma=2\)\(\lambda=4\)) & 87.83\% & 88.36\% & 80.21\% & 85.02\% \\ CEFL (\(\gamma=5\)\(\lambda=4\)) & 87.12\% & 88.77\% & 81.79\% & 85.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recall rates based on different loss functions of various ResNets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline Not CEFL loss & 72.39\% & 73.28\% & 72.98\% & 75.42\% \\ CEFL loss (\(\gamma=1\lambda=4\)) & 89.87\% & 85.25\% & 82.66\% & 84.61\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 84.21\% & 85.42\% & 86.29\% & 85.51\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 90.26\% & 89.84\% & 90.17\% & 91.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Recall rates for each classification model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline Not CEFL loss & 72.39\% & 73.28\% & 72.98\% & 75.42\% \\ CEFL loss (\(\gamma=1\lambda=4\)) & 89.87\% & 85.25\% & 82.66\% & 84.61\% \\ Improved CBAM module & 84.21\% & 85.42\% & 86.29\% & 85.51\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 90.26\% & 89.84\% & 90.17\% & 91.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: F1-score for each classification model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline Not CEFL loss & 73.77\% & 73.72\% & 75.61\% & 77.74\% \\ CEFL loss (\(\gamma=1\lambda=4\)) & 89.87\% & 85.25\% & 82.66\% & 84.61\% \\ Improved CBAM module & 84.21\% & 85.42\% & 86.29\% & 85.51\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 91.14\% & 90.68\% & 91.44\% & 92.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy rates for each classification model.