Various computer systems have attracted more researchers’ attention to arrive at a qualitative diagnosis in a few times. Different brain tumor classification approaches are proposed due to lesion complexity. This complexity makes the early tumor diagnosis using magnetic resonance images (MRI) a hard step. However, the accuracy of these techniques requires a significant amelioration to meet the needs of real-world diagnostic situations. We aim to classify three brain tumor types in this paper. A new technique is suggested which provides excellent results and surpasses the previous schemes. The proposed scheme makes use of the normalization, dense speeded up robust features, and histogram of gradient approaches to ameliorate MRI quality and generate a discriminative feature set. We exploit support vector machine in the classification step. The suggested system is benchmarked on an important dataset. The accuracy achieved based on this scheme is 90.27%. This method surpassed the most recent system according to experimental results. The results were earned through a strict statistical analysis (k-fold cross-validation), which proves the reliability and robustness of the suggested method.

中文翻译：

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基于混合方法的脑肿瘤分类

各种计算机系统吸引了更多研究人员的注意力，以期在几次内得出定性诊断。由于病变的复杂性，提出了不同的脑肿瘤分类方法。这种复杂性使得使用磁共振图像 (MRI) 进行早期肿瘤诊断变得困难。然而，这些技术的准确性需要显着改进才能满足现实世界诊断情况的需要。我们的目标是在本文中对三种脑肿瘤类型进行分类。提出了一种新技术，它提供了出色的结果并超越了以前的方案。所提出的方案利用归一化、密集加速鲁棒特征和梯度直方图方法来改善 MRI 质量并生成判别性特征集。我们在分类步骤中利用支持向量机。建议的系统在一个重要的数据集上进行了基准测试。基于该方案实现的准确率为90.27%。根据实验结果，该方法超过了最新的系统。结果是通过严格的统计分析（k 折交叉验证）获得的，这证明了所建议方法的可靠性和稳健性。