## 3 Results
The results indicate that modifying the kernel size and dropout rate has enhanced segmentation outcomes regarding DSI and mean IoU, mainly observed in the first proposed network, U-Net_P1. This modification process has improved results, notably strengthening the dice similarity index. Further improvement was achieved by altering the layers, such as replacing the max pooling layer with an average pooling layer and changing the transfer function to Leaky ReLU from ReLU. The latest modification to the U-Net structure involved adjusting the kernel size to 5 \(\times\) 5, setting the number of channels to 32, and the dropout ratio to 0.3. This modification significantly improved brain tumor segmentation, as demonstrated in the following table (Table 6): \begin{table}
\begin{tabular}{c c} \hline \hline
**Modified U-Net** & **Time (minutes)** \\ \hline U-Net\_P1 & 24 \\ U-Net\_P2 & 41 \\ U-Net\_P3 & 21 \\ U-Net\_P4 & 23 \\ U-Net\_P5 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Modified U-Net with various parameters. Figure 4: The graphical representation of the metrics in brain tumor segmentation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Global Accuracy** & **Mean Accuracy** & **MeanIoU** & **WeightIoU** & **MeanBF** & **DSC** \\ \hline U-Net\_P1 & **99.4\%** & 88.9\% & **84.1\%** & **98.8\%** & **86.3\%** & **88.3\%** \\ U-Net\_P2 & 99.2\% & 82.6\% & 79.5\% & 98.5\% & 83.2\% & 85.1\% \\ U-Net\_P3 & 97.5\% & **93.0\%** & 67.8\% & 96.5\% & 66.4\% & 78.6\% \\ U-Net\_P4 & 98.9\% & 92.0\% & 78.0\% & 98.1\% & 78.4\% & 85.4\% \\ U-Net\_P5 & 99.1\% & 92.5\% & 81.0\% & 98.5\% & 82.2\% & 87.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance of the five modified versions of U-Nets. The superior results achieved with the lowest training time of 19 min compared to existing methods surpass the standards in the literature. The experiment involved numerous parameter modifications, and the heat map images depicted the performance of each proposed model in delineating the tumor region. The accuracy of tumor segmentation, whether for small or large tumors, primarily hinges on the efficacy of the designed model in extracting local features and classifying pixels within the kernel structure's field of view, in addition to the dropout ratio. The results are visually depicted in the color map in the figure. Figure 6 shows the test image and its corresponding label. Figure 7 displays the segmentation results of various models for the original image, as shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Global Accuracy** & **Mean Accuracy** & **MeanIoU** & **WeightedIoU** & **MeanBF** & **DSC** \\ \hline U-Net\_P6 (Enhanced Model) & 99.5\% & 89.1\% & 85.8\% & 99.0\% & 88.5\% & 90.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of the Modified U-Net. Figure 5: The graphical representation of metrics by various modifications on the original U-Net structure. Figure 6: (**a**) The original test image. (**b**) The corresponding label. The heat map localization in each case does not precisely indicate the tumor region; it either extends to other areas or fails to cover the entire region. However, the last  
Figure 7: The heat map of segmentation results for (**a**) ResNet-18, (**b**) ResNet_50, (**c**) MobileNet, (**d**) Unet_P1, (**e**) U-Net_P2, (**f**) U-Net_P3, (**g**) U-Net_P4, (**h**) U-Net_P5, and (i) U-Net_Enhanced. Figure demonstrates the effectiveness of the Modified U-Net in accurately covering the whole region without compromising the tumor site. This improvement stems from the new modification's ability to extract the most relevant features using the kernel size and activation function, enhancing existing results and reducing computation time. The improved deep learning model effectively detects small tumors, not just large ones, as demonstrated in Figure 7, but also small tumors, as is evident in the corresponding Figure.