## 1 Introduction
### Literature Review  
There is potential for improving resource allocation in healthcare settings and expediting diagnostic procedures by incorporating machine learning algorithms into current neuroimaging workflows [13; 14]. By automating the initial screening and triage of imaging studies, these algorithms enable radiologists and clinicians to prioritize cases requiring urgent attention, thereby expediting the diagnostic pathway for patients at risk of brain tumors. Furthermore, the quantitative insights machine learning models provide can aid in risk stratification and treatment planning, guiding healthcare providers in selecting the most appropriate therapeutic interventions for individual patients [15; 16; 17]. Despite the considerable progress achieved in brain tumor detection through machine learning, several challenges and opportunities for further research persist. Chief among these is the need for large-scale, annotated datasets encompassing diverse populations and tumor subtypes, essential for training robust and generalizable algorithms. Additionally, efforts to enhance the interpretability and transparency of machine learning models are crucial for fostering trust and acceptance within the medical community, where clinical decision-making carries profound implications for patient outcomes. Brain tumor segmentation, a crucial task in medical image analysis, has seen significant advancements propelled by deep learning techniques. Various approaches have been explored to enhance the accuracy and efficiency of brain tumor segmentation algorithms. [PERSON] and [PERSON] [18] introduced a Modified U-Net and ResNet model, addressing the challenge of high-resolution MRI scans. By incorporating skip connections between different parts of the network, their model improves prediction accuracy and detail preservation compared to traditional U-Net architectures. The results from their study showed an Intersection over Union (IoU) of 90.2% and a dice coefficient of 94.8%. However, they did not provide any information regarding the size of the training and testing datasets. [PERSON] and [PERSON] [19] proposed a multi-input U-Net model integrating mask images to provide spatial relationship information, thereby improving segmentation accuracy, especially in regions with fuzzy boundaries. Additionally, their model tackles the low-resolution issue in sagittal and coronal planes, enhancing memory efficiency. A small dataset has been utilized, and their accuracy did not exceed 92%. [PERSON] and [PERSON] [20] developed a cascaded, Fully Convolutional Improved DenseNet with an Attention-based Adaptive Swin U-Net-derived segmentation strategy, achieving high precision and accuracy rates in tumor detection and segmentation tasks. The Brain MRI Dataset of Multiple Sclerosis has been exploited with 96.07% accuracy. Efforts have also been made to optimize brain tumor segmentation models for efficient hardware implementation. [PERSON] et al. [21] optimized a U-Net model for FPGA-based inference, reducing depth and filter count to enhance efficiency on FPGA hardware. Leveraging High-Level Synthesis for Machine Learning (HL54 ML), their implementation demonstrated significant reductions in resource utilization while maintaining segmentation accuracy comparable to the original model. A low result was achieved in the dice coefficient and did not exceed 74%. Additionally, [PERSON] and [PERSON] [22] proposed a deep transfer learning model combining U-Net and chopped VGGNet for brain tumor segmentation and classification. Their two-stage framework utilizes U-Net for segmentation and VGGNet for classification, but no results are mentioned in their paper. Innovative architectures have also emerged to address limitations in conventional convolutional networks. [PERSON] et al. [23] introduced BTSwin-U-Net, a 3D U-shaped symmetrical Swin Transformer-based network for brain tumor segmentation.