Gliomas are one of the most dangerous brain tumours, which are life-threatening in their highest grade. Manual diagnosis and detection of brain tumours are time-consuming. Thus, early and accurate detection of such tumours becomes essential for its prognosis. In this work, we aim to develop a fully automated glioma grade classification framework. The proposed method aims to learn relevant and non-redundant features from magnetic resonance brain tumour images. Gabor filter banks extract discriminant features from the brain images, and locality preserving projections are utilized for feature reduction to achieve non-redundant features. We train Gaussian radial basis function–support vector machine classifiers using these features for classifying gliomas into low grade glioma (LGG) or high-grade glioma. We apply Synthetic Minority Over-Sampling Technique on the minority class (LGG) to mitigate the class imbalance problem. The performance of the proposed method is validated by conducting fivefold cross-validation. We evaluate the performance of our proposed framework on different BRATS datasets: BRATS 2013, BRATS 2015, and BRATS 2017. We perform extensive experiments on each dataset separately, and experimental findings demonstrate that our proposed approach provides significantly superior performance compared to state-of-the-art techniques.

中文翻译：

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采用局部保持投影和高斯径向基函数支持向量机的二元胶质瘤分级框架

神经胶质瘤是最危险的脑肿瘤之一，最高级别会危及生命。手动诊断和检测脑肿瘤非常耗时。因此，早期准确检测此类肿瘤对其预后至关重要。在这项工作中，我们的目标是开发一个完全自动化的神经胶质瘤等级分类框架。所提出的方法旨在从磁共振脑肿瘤图像中学习相关和非冗余特征。Gabor 滤波器组从大脑图像中提取判别特征，并利用局部保留投影进行特征减少以实现非冗余特征。我们训练高斯径向基函数——支持向量机分类器，使用这些特征将神经胶质瘤分类为低级神经胶质瘤 (LGG) 或高级神经胶质瘤。我们在少数类 (LGG) 上应用合成少数类过采样技术来缓解类不平衡问题。通过进行五重交叉验证来验证所提出方法的性能。我们评估了我们提出的框架在不同 BRATS 数据集上的性能：BRATS 2013、BRATS 2015 和 BRATS 2017。我们分别对每个数据集进行了大量实验，实验结果表明我们提出的方法与状态相比提供了显着优越的性能-艺术技术。