Medical Image Segmentation for Brain Tumor Detection Using U-NET

Abstract

Abstract: Brain tumors encompass a wide variety of growths that can occur in the cranial cavity. These tumors can be benign or malignant, and early identification is important for suitable treatment. MRI is a non-invasive imaging method that provides detailed anatomical information and is useful in diagnosing and monitoring brain tumors. However, identifying tumors from complex MRI data remains a difficult and challenging task. This project uses image segmentation techniques to identify and identify tumors on MRI scans. The technology is works on the principles of deep learning, an intelligence process that enables computers to learn and recognize patterns in large data sets. This learning process helps doctors make informed decisions by distinguishing between healthy brain tissue and tumor growth. The unique design of the U-Net architecture, characterized by a U-shaped pattern with compactness and detail, makes it possible to capture complex patterns and fine details in medical images. Using different data from many brain MRI scans, the U-Net algorithm was trained to depict tumor regions with pixel-level accuracy.