Optimized Hybrid Kernel Support Vector Machine Framework for Multi-Class Brain Tumor MRI Classification

Abstract

Brain tumor remains one of the most life-threatening diseases worldwide, with early detection and accurate classification still posing major clinical challenges due to complex tissue structures and overlapping visual characteristics in MRI scans. To address this issue, this study proposes a Hybrid Kernel Support Vector Machine (HK-SVM) framework that integrates multiple kernel functions to improve classification performance and tumor localization. The hybridization of nonlinear and linear kernels enhances the model’s ability to capture diverse spatial and textural features, enabling more accurate separation of glioma, meningioma, and pituitary tumor classes. Experimental results show that the Sigmoid and RBF hybrid kernel achieved the best performance, reaching 99.3% accuracy along with precision, recall, and F1 score values of 99%, outperforming other kernel combinations. In addition, a region-based segmentation process was applied to visualize the detected tumor area, demonstrating close alignment with ground truth masks. The proposed method contributes to developing a more interpretable and computationally efficient MRI-based diagnostic framework, offering a reliable alternative to deep learning approaches for medical image classification and paving the way for future integration with deep models such as U-Net or transfer learning CNNs for enhanced segmentation and feature extraction.