MRI Image Segmentation of Brain Tumors Using the U-Net Model

Segmenting a brain tumour is an intriguing and important topic in medicine. Brain tumour segmentation's primary goal is to find the location and extent of brain tumour, which aids in its successful therapeutic therapy. When cells in brain or central nervous system develop abnormally then there is a chance of occurrence of brain tumour. One can categorise it as either primary or metastatic. Utilising MRI (Magnetic Resonance Imaging) is common ways to identify brain tumours. But because various radiologists could have different viewpoints, it is better to employ cutting-edge model for brain tumour segmentation, like the U-Net architecture. Our proposed U-Net model was trained and validated on MRI datasets, achieving better performance than existing methods like Thresholding, K-Means, and Fuzzy C often produce noisy and inconsistent results. Deep learning approaches, especially U-Net architecture, have shown superior accuracy in medical image segmentation due to their encoder-decoder design and ability to capture spatial features effectively. Comparative results show that U-Net delivers cleaner, more accurate tumor segmentation, making it a reliable tool in clinical practice.