脑肿瘤的术前诊断对于治疗计划很重要，因为它有助于肿瘤的预后。近几年来，人工智能和机器学习领域的发展为医学领域做出了巨大贡献，特别是通过放射图像和磁共振图像对脑肿瘤的分级进行诊断。由于医学图像中肿瘤描述符的复杂性，评估神经胶质瘤的准确分级对医生来说是一个重大挑战。我们通过将新的 MRI 特征与集成学习方法相结合，提出了一种新的神经胶质瘤分级分类系统，称为基于自适应功率均值组合器的集成学习 (EL-APMC)。我们评估并比较 EL-APMC 算法与代表最先进机器学习算法的 21 个分类器模型的性能。结果表明，EL-APMC 算法在名为 BRATS2015 的 MRI 脑肿瘤数据集上实现了分类准确率 (88.73%) 和 F1 分数 (93.12%) 方面的最佳性能。此外，我们还表明，22 个分类器模型之间的分类结果差异具有统计显着性。我们相信 EL-APMC 算法是小数据集分类的有效方法，这在医学领域很常见。所提出的方法为神经胶质瘤的分类提供了一个有效的系统，具有高可靠性和准确的临床结果。© 2024。作者。

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors' prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.© 2024. The Author(s).