Glioma Classification using Multi-sequence MRI and Novel Wavelets-based Feature Fusion

Abstract

Glioma, a prevalent and heterogeneous tumor originating from the glial cells can be differentiated as Low-Grade Glioma (LGG)and High-Grade Glioma (HGG) according to World Health Organization’s norms. Classifying gliomas is essential for treatment protocols that depends extensively on subtype differentiation. For non-invasive glioma evaluation, Magnetic Resonance Imaging (MRI) offers vital information about the morphology and location of the tumor. The versatility of MRI allows the classification of gliomas as LGG and HGG based on their texture, perfusion, and diffusion characteristics and further for improving the diagnosis and providing tailored treatments. Nevertheless, the precise classification is complicated by tumor heterogeneity and overlapping radiomic characteristics. Thus, in this work wavelet based novel fusion algorithm were implemented on multi-sequence T1, T1-contrast enhanced (T1CE), T2 and Fluid Attenuated Inversion Recovery (FLAIR) MRI images to compute the radiomics features. Furthermore, principal component analysis is applied to reduce the feature space and XGBoost, Support Vector Machine, and Random Forest Classifier are used for the classification. The result shows that the SVM algorithm performs comparatively well with an accuracy of 90.17%, precision of 91.04%, and recall of 96.19%, F1-score of 93.53%, and AUC of 94.60% when implemented on BraTS 2018 dataset and with an accuracy of 91.34%, precision of 93.05%, recall of 96.13%, F1-score of 94.53%, and AUC of 93.71% for BraTS 2019 dataset. Thus, the proposed algorithm could be potentially implemented for the computer-aided diagnosis and grading system for gliomas.