Brain Tumor Segmentation: U-Net Architecture for MRI Precision Diagnostics
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Abstract
Abstract : Brain tumor segmentation is a critical step in the diagnosis and treatment planning for patients. This study explores the efficacy of the U-Net architecture, a deep learning model specifically designed for biomedical image segmentation, in accurately distinguishing tumor regions in MRI scans. We utilized a dataset comprising diverse MRI images annotated by specialists, ensuring comprehensive training for the U-Net model. Our findings demonstrated that the U-Net architecture significantly improves segmentation accuracy and enhances the visualization of tumor margins compared to conventional methods. Furthermore, we analyzed the model’s performance across varying tumor types and sizes, revealing its robustness and adaptability in clinical applications. By integrating advanced image analysis techniques into MRI diagnostics, this research offers a promising approach to support radiologists in making informed decisions, ultimately leading to improved patient outcomes. Future work will focus on refining the model and exploring its integration into routine clinical workflows.
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