Faculty of Engineering and Natural Sciences, Electrical & Electronics Engineering Department, Konya Technical University, 42250, Konya, Turkey.
Faculty of Engineering and Architecture, Biomedical Engineering Department, Necmettin Erbakan University, Konya, Turkey.
Med Biol Eng Comput. 2020 Dec;58(12):2971-2987. doi: 10.1007/s11517-020-02273-y. Epub 2020 Oct 2.
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. Graphical abstract.
由于肿瘤的复杂性，脑肿瘤的二进制分类具有挑战性。这些挑战源于同类型肿瘤的形状、大小和强度特征的多样性。因此，框架设计应该针对两个现象进行优化：特征分析和分类。基于该问题的挑战和难度，目前存在的有限信息或研究都考虑了三维（3D）脑肿瘤的二进制分类。在本文中，通过设计基于 3D 磁共振成像（3D MRI）数据的各种框架，完成了高级别胶质瘤（HGG）和低级别胶质瘤（LGG）的区分。相应地，集成了不同的相位组合、特征排序方法和混合分类器。通过使用一阶统计量（FOS）进行特征分析，通过检查单一相位（T1c、FLAIR、T1 和 T2）附近不同相位组合的使用情况，以及考虑五种特征排序方法（Bhattacharyya、熵、ROC、t 检验和 Wilcoxon）来检测适当的输入到分类器，从而实现了显著的性能。由于其在医学模式分类方面的稳健性和优越性，基于神经网络（NN）的混合分类器被认为是可行的。在这项研究中，使用了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）和基于高斯映射的混沌粒子群优化（GM-CPSO）。集成框架，包括 DW-PSO-NN、CDW-PSO-NN 和 GM-CPSO-NN，在涉及 210 个 HGG 和 75 个 LGG 样本的 BraTS 2017 挑战赛数据集上进行了评估。使用 2 折交叉验证测试方法和七个指标（准确性、AUC、敏感性、特异性、g-均值、精度、f-度量）来评估框架的性能。在实验中，使用 FOS、包括三个相位组合的数据、Wilcoxon 特征排序方法和 GM-CPSO-NN 方法的最有效框架被提供。因此，我们的框架在 3D 脑 MRI 数据的 HGG/LGG 分类中取得了显著的成绩，包括 90.18%（准确性）、85.62%（AUC）、95.24%（敏感性）、76%（特异性）、85.08%（g-均值）、91.74%（精度）和 93.46%（f-度量）。
