## 3 Results
Figure 8 shows the original image, the ground truth tumor region, the network's predicted label, and a heat map representation to highlight the exact affected area, as indicated by the heat map bar on the far right of the Figure. The current study is compared with the literature to identify its strengths and weaknesses. Table 7 describes the study number alongside the type of database used to conduct the study. The dataset used in these studies is the same data utilized to evaluate the enhanced model. The corresponding table details all studies that employed this dataset. As evident from the previous table, numerous papers have used the same dataset in recent years but achieved results that are inferior to ours. The only study with a higher MDice is reference [30]. However, this study did not use a validation set and had a smaller test sample size than ours. Researchers tested on 289 images, whereas we tested \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Method**}} & \multicolumn{4}{c}{**\# of Dataset**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{2-10}  & **Training** & **Validation** & **Test** & **MloU (\%)** & **MDice (\%)** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline
[30] & SCU-Net & 2475 & - & 289 & 86.8\% & 92.6\% & - & - & - \\ \hline
[30] & U-Net & 2475 & - & 289 & 76.3\% & 85.9\% & - & - & - \\ \hline
[31] & encoder-decoder-based U-Net with residual network & 2451 & 306 & 307 & - & 90.1\% & - & - & - \\ \hline
[32] & (IIB)-based Deep Residual network model & 2757 & - & 307 & - & 92.6\% & 90.0\% & 91.5\% \\ \hline
[33] & CycleGAN & - & - & - & 74.4\% & - & - & - \\ \hline
[34] & U-Net & 2451 & - & 613 & & 76.0\% & 90.0\% & - & - \\ \hline Proposed Network & Enhanced Unet & 2145 & **306** & 613 & 85.8\% & **90.2\%** & 99.5\% & 90.2\% & **99.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparison between the current and previous studies for the same dataset. Figure 8: Testing the modified version on small tumors, (**a**) the original image, (**b**) the ground truth tumor, (**c**) the predicted tumor using the enhanced model, and (**d**) the color representation of the exact affected region in the brain. on 613 images, significantly affecting the dice results. Additionally, previous studies in the literature did not mention their results clearly and often used different datasets with smaller sample sizes. The key strengths of our study are its simplicity, low computation time, and precise results. A notable difference emerges when comparing the current study to the nnU-Net. The modified version is optimized specifically for brain tumors and can be applied to other datasets. In contrast, the automated adjustment of parameters in nnU-Net to achieve the best segmentation results and changing layers requires significant computation time, which is costly in medical diagnostics. The enhanced version of U-Net uses Leaky ReLU instead of ReLU, offering advantages like improved model robustness and performance by addressing limitations such as vanishing gradients for negative inputs. With fixed parameters in the modified version, it delivers robust and fast results, making it suitable for hospital use and training radiology residents. Furthermore, the modified model was compared with the nnU-Net used in [36; 37], which was applied to the [PERSON] 2020 brain tumor dataset. The nnU-Net achieved a dice coefficient not exceeding 85%, whereas the modified version of U-Net reached a dice coefficient of 90.2% with 5.5 M parameters. In contrast, the nnU-Net had 25.71 M parameters, indicating its high complexity and longer computation time required to optimize hyperparameters. By fixing hyperparameters such as learning rate, batch size, and the optimizer, time is saved, and efforts can be focused on adjusting the model weights to achieve the best results.