Altıok, ÖGüngör, MA2026-03-312026-03-3120252076-3417http://dx.doi.org/10.3390/app16010219https://hdl.handle.net/11491/9562Meningiomas are the most common primary brain tumors in the central nervous system. Although numerous studies in the literature have addressed multiclass brain tumor classification that includes the meningioma class, the method proposed in this study aims to improve meningioma detection performance by employing binary classification instead of multiclass classification. The proposed method enhances classification performance by implementing a three-step classification process. This study utilizes the Nickparvar dataset, which contains brain Magnetic Resonance (MR) images of meningioma, other tumor types, and tumor-free cases. We employ k-means clustering for tumor segmentation, GLCM and contour features for feature extraction, and CatBoost for classification (meningioma vs. non-meningioma). The performance of the proposed method is evaluated using accuracy, precision, recall, negative predictive value, F1-score, and specificity, achieving values of 0.96, 0.93, 0.89, 0.97, 0.91, and 0.98, respectively. Although deep learning methods demonstrate high performance, machine learning approaches require less training data and computational resources. Therefore, machine learning methods represent a more suitable choice for clinical environments with limited hardware capabilities. The results are comparable to those of recent deep learning studies, indicating that the proposed method achieves performance close to deep learning approaches while retaining the advantages of machine learning for meningioma detection.enbrain tumorimage classificationimage segmentationmachine learningmagnetic resonance imagingArticle16110.3390/app16010219WOS:001657225000001