## 3 Results  
The available dataset is divided into three subsets: 70% for training with 2415 images, 20% for testing with 613 images, and 10% for validation with 305 images. The proposed workflow was trained, validated, and tested using hyperparameters, including RMSprop optimizer, a learning rate of 0.0001, 100 epochs, and a mini-batch size of 32. The process began with existing deep labv3 models, specifically ResNet-18, MobileNet, ResNet-50, and the original U-Net. This paper is carried out using a work station two VGA cards with processors 13 Gen Intel Core i9 13900K, with memory 4\(\times\) 32 GB FURY 6000 MHz DDR5, and internal drive 2\(\times\) Samsung 980 PRO 2 TB M.2 NVME GEN 4. The table below illustrates the performance of each model under these settings. MobileNet demonstrated the best Global Accuracy and Mean Accuracy, Weighted IoU, and achieved the second highest rank in DSI. ResNet-18 outperformed ResNet-50 and nearly matched U-Net in various performance metrics. Due to the numerous advantages of U-Net's encoder and decoder structures, this paper focuses on modifying the designed U-Net to create a more accurate, faster, and lighter model. The experiment involved multiple parameter modifications to enhance the existing U-Net's performance. Each modification is detailed in terms of kernel size, number of channels, and dropout ratio before the connected layer. Table 2 presents the results of the original U-Net without any modifications, with parameters including a kernel size of 3 \(\times\) 3, 64 channels, and a dropout ratio of 0.5. As shown in Table 2, its performance is comparable to MobileNet. In contrast, Table 3 highlights the differences between the results obtained from MobileNet and U-Net. The primary difference lies in computation time: MobileNet requires nearly one hour to build the model, whereas U-Net has the lowest computation time among all structures, taking only 27 min. This indicates a more straightforward structure. Therefore, modifications to the existing U-Net structure were made to optimize it for better results while maintaining lower computation time, which is the primary goal of this paper. Table 2 lists each modification by name and number and the specific parameters altered. Furthermore, Figure 4 depicts the results presented in Table 2. The bold number indicates to lowest computation time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Global Accuracy** & **Mean Accuracy** & **MeanIoU** & **WeightIoU** & **MeanBF** & **DSC** \\ \hline ResNet-18 & 99.3\% & 89.8\% & 83.4\% & 98.8\% & 83.2\% & 87.8\% \\ \hline ResNet-50 & 99.2\% & 92.3\% & 81.4\% & 98.5\% & 79.5\% & 86.7\% \\ \hline MobileNet & 99.4\% & 89.0\% & 84.0\% & 98.8\% & 84.6\% & 88.2\% \\ \hline U-Net & 99.3\% & 86.3\% & 82.0\% & 98.7\% & 85.5\% & 86.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of various models on the previous settings. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Time Computation (minutes)** \\ \hline ResNet18 & 31 \\ ResNet50 & 35 \\ MobileNet & 54 \\ U-Net & **27** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The computation time for each deep learning model. Each proposed model was trained using the previously mentioned hyperparameters (optimizers, number of epochs, learning rate). The training time for each model was recorded, and the results are summarized in Table 4.  
The previous table clearly shows the effectiveness of reducing the kernel size, which is the field of view of the filter and adjusting the number of depth channels and the dropout ratio during training time. The shortest training time was achieved by U-Net_P3, completing training within 21 min. The performance criteria for each model were computed, and the results are summarized in Table 4.  
The results in Table 5 are presented visually in Figure 5, as shown below. The bold indicates to the highest result in each column.