## 4 Conclusions  
Detecting brain tumors is a critical task in medical diagnostics due to the severe implications these anomalies pose to patients' health and well-being. The human brain, with its intricate network of neurons and supporting structures, is susceptible to various pathological conditions, with tumors being particularly challenging. Employing artificial intelligence aims to detect and segment brain tumor regions accurately. This study evaluated the performance of deep learning models for accurate segmentation of brain tumors using a large dataset. It began by examining existing models and their efficiency in precise segmentation. The study utilized the U-Net model with various modifications to achieve the best Enhanced U-Net model for segmentation.  
The focus was on specific parameters such as the number of channels, dropout ratio, kernel size, activation function type, and pooling layer type. Training options were standardized across all trials to evaluate the impact of these modifications on performance results. The optimal model was achieved by combining a 5 \(\times\) 5 kernel size, 32 channels, a 0.3 dropout ratio, average pooling instead of max pooling, and Leaky ReLU instead of ReLU. The results showed significant improvement over previous literature, with training time not exceeding 19 min. The performance metrics revealed a dice similarity of 90.2% and an accuracy of 99.5%. The modified version's performance was tested on both large and small tumors, which presents a significant challenge for radiologists. In all cases, the model demonstrated notable results in delineating the tumor region and identifying the major affected areas.  
This proposed model has the potential to be developed into standalone software for use in hospitals and clinics, serving as a valuable tool for radiologists in identifying tumor locations for further diagnosis. It can benefit resident radiologists by enhancing their experience pinpointing tumor locations and reducing errors in identifying suspicious regions.  
Conceptualization, H.A. and M.A.; methodology, H.A.; software, H.A. and M.A.; validation, H.A., A.R. and M.A.; formal analysis, H.A. and M.A.; investigation, H.A., A.R., W.A.M. and M.A.; resources, M.A.; data curation, M.A.; writing--original draft preparation, H.A., A.R. and W.A.M.; writing--review and editing, A.R. and W.A.M.; visualization, H.A.; supervision, H.A.; project administration, H.A.; funding acquisition, H.A. All authors have read and agreed to the published version of the manuscript.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The dataset used in this study is a 2D brain tumor segmentation dataset. It is publicly available on Kaggle at the following link: [[https://www.kaggle.com/datasets/nikhilroxtomar/brain-tumor-segmentation/data](https://www.kaggle.com/datasets/nikhilroxtomar/brain-tumor-segmentation/data)]([https://www.kaggle.com/datasets/nikhilroxtomar/brain-tumor-segmentation/data](https://www.kaggle.com/datasets/nikhilroxtomar/brain-tumor-segmentation/data)) (20 September 2023). Conflicts of Interest: The authors declare no conflicts of interest.