Abstract
The human brain plays a vital role in regulating bodily functions, and abnormal cell growth may lead to life-threatening brain tumors. Automated computer-aided diagnosis systems are therefore essential to support early detection from MRI images. This study investigates brain tumor classification using Gray Level Co-occurrence Matrix (GLCM) feature extraction combined with Support Vector Machine (SVM) classification. Unlike prior works that typically employ a single kernel configuration, this study conducts a systematic comparison of four SVM kernels: linear, polynomial, radial basis function (RBF), and sigmoid under a consistent preprocessing pipeline and structured hyperparameter tuning framework. GLCM features, including energy, contrast, correlation, and homogeneity, were extracted at multiple distances and angles. Kernel performance was evaluated using controlled hyperparameter search procedures to ensure fair comparison across models. Experimental results on a binary MRI dataset consisting of 2,800 images demonstrate that the RBF kernel achieved the highest accuracy of 96% with C = 100 and gamma = 10, outperforming polynomial (74%), linear (72%), and sigmoid (71%) kernels. The findings highlight the importance of systematic kernel evaluation and parameter sensitivity analysis in texture-based medical image classification. The proposed GLCM–SVM framework provides a computationally efficient and interpretable approach that may support preliminary decision-aid systems for brain tumor screening.