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This research aims to enhance brain tumor classification and segmentation using a new geometric deep learning framework.

Mod-SE(2): a geometric deep learning framework for brain tumor classification and segmentation in MRI images

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Geometric deep learning improves tumor segmentation and classification in MRI images, indicating enhanced interpretability and performance.

This research aims to enhance brain tumor classification and segmentation using a new geometric deep learning framework.
Introduced Modified Special Euclidean (Mod-SE(2)) framework
Implemented symmetry-preserving group convolutions and spatial priors
Evaluated on three MRI datasets and benchmarked against U-Net, VGG16, ResNet.
Mod-Cls-SE(2) achieved 0.914 classification accuracy, outperforming existing architectures
Segmentation achieved a dice coefficient of 0.9503 and IoU of 0.9616 on BraTS2020 dataset
Reduced inference time while demonstrating strong computational performance.

Angelina et al. (2026) studied this question.

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Geometric deep learning improves tumor segmentation and classification in MRI images, indicating enhanced interpretability and performance.