## 3 Results  
During this research, we performed a comparative analysis utilizing three different models. These models were evaluated on a laptop equipped with an Nvidia GeForce RTX 3070 Ti Laptop 16 GB GPU and 32 GB RAM. We gauged the model's performance based on crucial metrics such as loss, accuracy, precision, recall, and the F1-score. Model 1, constructed with gray image data, data augmentation, and background removal, was initially trained using gray image data for 200 epochs. This led to a loss of 7.622 and an accuracy of 70.86% A subsequent training period of 200 epochs with data augmentation resulted in an increased loss of 8.8518 and a marginal decrease in accuracy to 70.64%. However, a notable performance improvement was observed following training with background removal data for 10 epochs. This process resulted in a significant reduction in loss to 1.5001 and a considerable increase in accuracy to 90.39%. Consequently, Model 1 displayed satisfactory results, achieving an accuracy of 90.39%. Figure 8 and Table 3 illustrate the experimental results, confusion metrics, and F1-score for Model 1. Model 2, which utilized normal image data, data augmentation, and background removal, was initially trained with normal image data for 200 epochs. This resulted in a loss of 1.2343 and an accuracy of 0.7325. An additional 200 epochs of data augmentation  
Figure 8: The confusion matrix of Model 1-3. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Model 1**} \\ \hline  & \multicolumn{3}{c}{**Model 1-1**} & \multicolumn{3}{c}{**Model 1-2**} & \multicolumn{3}{c}{**Model 1-3**} \\ \hline
**Level of DR** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline
0 & 0.76 & 0.93 & 0.83 & 0.75 & 0.93 & 0.83 & 0.91 & 0.98 & 0.95 \\ \hline
1 & 0.11 & 0.02 & 0.03 & 0.12 & 0.02 & 0.04 & 0.94 & 0.76 & 0.84 \\ \hline
2 & 0.25 & 0.13 & 0.17 & 0.25 & 0.12 & 0.17 & 0.83 & 0.8 & 0.82 \\ \hline
3 & 0.25 & 0.03 & 0.05 & 0.15 & 0.02 & 0.03 & 0.9 & 0.31 & 0.46 \\ \hline
4 & 0.1 & 0.01 & 0.01 & 0.29 & 0.03 & 0.05 & 0.89 & 0.1 & 0.19 \\ \hline
**Accuracy** & & & 70.86\% & & 70.64\% & & & **90.39\%** & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance metrics comparison for Model 1 and sub-models. increased the loss to 6.0087 but also marginally enhanced the accuracy to 0.7330. After training the model with background removal data for 10 epochs, the loss decreased to 1.2560, and the accuracy reached an impressive 0.9060. As a result, Model 2 also produced satisfactory results, with an accuracy of 90.60%. The experimental results, confusion metrics, and F1-score for Model 2 can be seen in Figure 9 and Table 4.  
Lastly, Model 3, which also employed normal image data, data augmentation, and background removal but with fewer epochs, was initially trained with normal image data for 30 epochs. This yielded a loss of 1.0508 and an accuracy of 0.7302. Further training with 40 epochs of data augmentation resulted in an increased loss of 4.0852 and a decreased accuracy to 0.7144. The final round of training with background removal data for 10 epochs brought the loss to 1.2684 and an accuracy of 0.8764. Despite the reduction in the number of epochs, Model 3 yielded less satisfactory results compared to the other two models. The experimental results, confusion metrics, and F1-score for Model 3 are illustrated in Figure 10 and Table 5.