Deep Learning-Driven MRI Image Segmentation and Classification for Brain Tumors Using RF, SVM, YOLOv5, and U-Net Architectures

<p>This work uses deep learning and machine learning approaches to identify and categorize brain cancers from MRI scans. U-Net is utilized for precise tumor segmentation, YOLOv5 is employed for real-time detection, and Random Forest (RF) and Support Vector Machines (SVM) are employed for tumor type classification. In order to assist doctors, in diagnosing brain tumors more rapidly, the system aims to automate segmentation, detection, and classification, improve diagnosis accuracy, and reduce analysis time. By developing early intervention strategies for brain tumor treatment, this study enhances patient care.</p>