A Comprehensive Study of Brain Tumour Discrimination Using Phase Combinations, Feature Rankings, and Hybridised Classifiers

Abstract

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,ttest, 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.