An Optimized Strategy for Brain Tumor Classification Using SO(3) Equivariant Graph Neural Networks with Snow Geese Algorithm in MRI Imaging

Key Points

• Classification accuracy reached 92% with the optimized strategy, indicating a significant improvement over traditional methods.
• Key evidence confirms that employing the snow geese algorithm enhances the training of graph neural networks effectively.
• The approach incorporates SO(3) equivariant graph neural networks, allowing for better handling of complex data structures in MRI imaging.
• Findings support advancing tumor classification techniques, suggesting broader applications in medical diagnostics using neural networks.