Brain Tumor Detection Using U-Net and SVM

Abstract

Brain tumors, particularly gliomas, pose a significant clinical challenge, requiring both precise localization and accurate grading to guide treatment. Accurate segmentation of tumor regions is a critical first step, enabling meaningful analysis and interpretation of the affected areas. In this project, we present a hybrid framework that first segments tumor regions in brain Magnetic Resonance Imaging (MRI) scans using a U-Net model trained on the Brain Tumor Segmentation dataset, and then classifies these regions as Low-Grade or High-Grade Gliomas with a Support Vector Machine (SVM) model based on features extracted from the segmented masks. On the held-out test set, our U-Net achieved an accuracy of 99.3%, while the SVM classifier delivered an overall accuracy of 93%.