## 3 Comparative Experiment
### Comparative Experiment
#### 3.3.2 Comparison Experiment of Pixel-Level Segmentation  
To compare the effect of path-level segmentation, we conducted extended experiments using pixel-level segmentation. We applied five networks for the comparative experiments: U-Net, U-Net++, SegNet, TransUnet, and Swin-UNet. We used these five networks to compare the performance of the CNN and ViT for pixel-level segmentation. U-Net, U-Net++, and SegNet stand for CNN network, Swin-UNet stands for transformer networks, and TransUnet stands for CNNs joining the transformer. Table 6 presents the outcome of the five model prediction metrics. We found that U-Net++ exhibited the highest segmentation performance overall, but also had the longest training time. U-Net exhibited the worst segmentation performance. The segmentation result of the vision transformer network (Swin-UNet) was second after that of U-Net++. The Jaccard and precision values were 71.26% and 85.00%, respectively, which were higher than those of the other network models. To compare the segmentation results more intuitively, we presented the pixel-level segmentation results in Figure 8. Clearly, the pixel-level segmentation results are better than those of the patch level. However, the patch-level segmentation effect was better for multiobject transparent microorganism images. Compared to the \(8\times 8\) patch-level segmentation, the network model with a transformer structure (Swin-UNet) at the pixel level performed well, and the ViT was higher than the accuracy of the CNN model. However, in the \(8\times 8\) patch-level experiment, the accuracy of the CNN was higher than that of ViT. It is shown in Figure 5 that the loss curve stability of Swin-UNet is significantly better than that of the other four models during training. The training loss stability of the ViT model was better than that of the CNN model. To reflect the training process of the model more intuitively, the _Intersection-over-Union_ (IOU) curves of the five models on the training and validation sets in the pixel-level experiment is presented in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Avg.Dice** & **Avg.Jaccard** & **Avg.Precision** & **Avg.Recall** & **Avg.Acc** \\ \hline U-Net & 71.82 & 59.23 & 68.98 & 76.06 & 91.93 \\ U-Net++ & 82.51 & 73.51 & 83.42 & 85.98 & 95.32 \\ SegNet & 78.21 & 67.70 & 77.45 & 84.66 & 74.06 \\ Trans-Unet & 75.50 & 64.13 & 72.52 & 86.75 & 93.44 \\ Swin-UNet & 81.00 & 71.26 & 85.00 & 82.08 & 95.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Segmentation performance of models of five-fold cross-validation experiment on the test set.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Class** & **Avg.Pre** & **Avg.Rec** & **Avg.Spe** & **Avg.F1** & **Avg.PAcc** \\ \hline \multirow{3}{*}{ResNet50} & FG & 62.2 & 87.2 & 90.6 & 72.6 & \multirow{3}{*}{90.0} \\  & BG & 97.5 & 90.6 & 87.2 & 93.9 & \\ \multirow{3}{*}{Inception-V3} & FG & 52.6 & 91.5 & 85.4 & 66.8 & \\  & BG & 98.3 & 85.4 & 91.5 & 91.4 & \\ \multirow{3}{*}{VGG-16} & FG & 60.7 & 89.4 & 89.7 & 72.6 & \\  & BG & 97.9 & 89.7 & 89.4 & 93.6 & \\ \multirow{3}{*}{Xception} & FG & 51.8 & 90.7 & 85.0 & 65.9 & \\  & BG & 98.1 & 85.0 & 90.7 & 91.1 & \\ \multirow{3}{*}{ViT} & FG & 60.4 & 83.8 & 90.2 & 70.2 & \\  & BG & 96.9 & 90.2 & 83.8 & 93.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance of models of five-fold cross-validation experiment on test set of \(8\times 8\) pixels’ patches. PAcc (prediction accuracy), FG (foreground) and BG (background) (In [%]).