The binary categorisation of brain tumours is challenging owing to the complexities of tumours. These challenges arise because of the diversities between shape, size, and intensity features for identical types of tumours. Accordingly, framework designs should be optimised for two phenomena: feature analyses and classification. Based on the challenges and difficulty of the issue, limited information or studies exist that consider the binary classification of three-dimensional (3D) brain tumours. In this paper, the discrimination of high-grade glioma (HGG) and low-grade glioma (LGG) is accomplished by designing various frameworks based on 3D magnetic resonance imaging (3D MRI) data. Accordingly, diverse phase combinations, feature-ranking approaches, and hybrid classifiers are integrated. Feature analyses are performed to achieve remarkable performance using first-order statistics (FOS) by examining different phase combinations near the usage of single phases (T1c, FLAIR, T1, and T2) and by considering five feature-ranking approaches (Bhattacharyya, Entropy, Roc, t test, and Wilcoxon) to detect the appropriate input to the classifier. Hybrid classifiers based on neural networks (NN) are considered due to their robustness and superiority with medical pattern classification. In this study, state-of-the-art optimisation methods are used to form the hybrid classifiers: dynamic weight particle swarm optimisation (DW-PSO), chaotic dynamic weight particle swarm optimisation (CDW-PSO), and Gauss-map-based chaotic particle-swarm optimisation (GM-CPSO). The integrated frameworks, including DW-PSO-NN, CDW-PSO-NN, and GM-CPSO-NN, are evaluated on the BraTS 2017 challenge dataset involving 210 HGG and 75 LGG samples. The 2-fold cross-validation test method and seven metrics (accuracy, AUC, sensitivity, specificity, g-mean, precision, f-measure) are processed to evaluate the performance of frameworks efficiently. In experiments, the most effective framework is provided that uses FOS, data including three phase combinations, the Wilcoxon feature-ranking approach, and the GM-CPSO-NN method. Consequently, our framework achieved remarkable scores of 90.18% (accuracy), 85.62% (AUC), 95.24% (sensitivity), 76% (specificity), 85.08% (g-mean), 91.74% (precision), and 93.46% (f-measure) for HGG/LGG discrimination of 3D brain MRI data.

由于肿瘤的复杂性，脑肿瘤的二元分类具有挑战性。这些挑战的出现是因为相同类型肿瘤的形状、大小和强度特征之间存在差异。因此，框架设计应该针对两种现象进行优化：特征分析和分类。基于该问题的挑战和困难，存在考虑三维 (3D) 脑肿瘤二元分类的有限信息或研究。在本文中，通过基于 3D 磁共振成像 (3D MRI) 数据设计各种框架来实现高级别胶质瘤 (HGG) 和低级别胶质瘤 (LGG) 的区分。因此，集成了不同的相位组合、特征排序方法和混合分类器。吨test 和 Wilcoxon）来检测分类器的适当输入。基于神经网络（NN）的混合分类器因其鲁棒性和医学模式分类的优越性而被考虑。在这项研究中，使用最先进的优化方法来形成混合分类器：动态权重粒子群优化（DW-PSO）、混沌动态权重粒子群优化（CDW-PSO）和基于高斯图的分类器混沌粒子群优化（GM-CPSO）。在涉及 210 个 HGG 和 75 个 LGG 样本的 BraTS 2017 挑战数据集上评估了包括 DW-PSO-NN、CDW-PSO-NN 和 GM-CPSO-NN 在内的集成框架。处理 2 折交叉验证测试方法和七个指标（准确度、AUC、灵敏度、特异性、g-mean、精度、f-measure）以有效评估框架的性能。在实验中，提供了使用 FOS 的最有效框架，数据包括三相组合、Wilcoxon 特征排序方法和 GM-CPSO-NN 方法。因此，我们的框架取得了 90.18%（准确度）、85.62%（AUC）、95.24%（敏感性）、76%（特异性）、85.08%（g-mean）、91.74%（精确度）和 93.46%（ f-measure) 用于 3D 脑 MRI 数据的 HGG/LGG 区分。