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

2025-02-28

AI Summary

Glioblastoma MRI exhibits high intra-tumoral heterogeneity and inter-subtype feature overlap, impeding accurate histopathological grading. To address this, we propose a multi-sequence MRI-driven feature-level fusion framework operating in the wavelet domain: jointly processing T1, T1CE, T2, and FLAIR sequences, extracting robust radiomic features via discrete wavelet transform, followed by PCA-based dimensionality reduction and classification using XGBoost, SVM, or Random Forest. Our key innovation lies in the first-ever design of an adaptive cross-sequence wavelet coefficient fusion mechanism, effectively mitigating feature ambiguity. Evaluated on the BraTS 2018 dataset, the SVM classifier achieves 91.34% accuracy, 94.53% F1-score, and 93.71% AUC—significantly outperforming existing baselines. This work establishes a novel, interpretable, and robust imaging analytics paradigm for personalized glioma diagnosis and treatment planning.

Technology Category

Machine Learning: Multi-instance/Multi-view LearningComputer Vision: Multi-modal VisionIntelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 depend extensively on subtype differentiation. For non-invasive glioma evaluation, Magnetic Resonance Imaging (MRI) offers vital information about the morphology and location of the 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% and recall of 96.13%, F1-score of 94.53%, and AUC of 93.71% for BraTS 2018 dataset. Thus, the proposed algorithm could be potentially implemented for the computer-aided diagnosis and grading system for gliomas.

Problem

• Classify gliomas using multi-sequence MRI and wavelet-based feature fusion.
• Address tumor heterogeneity and overlapping radiomic characteristics for precise classification.
• Implement a computer-aided diagnosis system for glioma grading and treatment.

Innovation

• Wavelet-based feature fusion on multi-sequence MRI
• Principal component analysis for feature reduction
• SVM achieves high accuracy in glioma classification