3W‐MultiHier: A Three Way Multi‐Hierarchical Model Enabled Deep Learning for Brain Tumor Classification in MRI Scans

Abstract

Accurate brain tumor detection and classification are vital for effective diagnosis and treatment planning in medical imaging. Despite advancements in deep learning, challenges such as multimodal complexity, small lesion segmentation, limited training data, and variability in tumor characteristics hinder precise tumor analysis in MRI scans. To address these issues, we propose the Three Way Multi‐Hierarchical Model (3W‐MultiHier) for tumor classification in MRI. 3W‐MultiHier employs a hybrid Capsule‐Transformer UNet (Capsule‐TransUNet) architecture, integrating capsule and transformer networks within the U‐Net framework. This enables the model to capture spatial hierarchies, long‐range dependencies, and global context, ensuring accurate tumor boundary segmentation. The model also incorporates Residual Network Version 2 ‐ Squeeze‐and‐Excitation Network (ResNetV2‐SENet), which excels at extracting complex features through deep hierarchical structures and feature recalibration. Additionally, the Vision Transformer ‐ Transfer Learning (ViT‐TL) pipeline enhances classification accuracy by leveraging fine‐grained hierarchical representations. Extensive evaluations on BraTS (2019, 2020, 2021) datasets demonstrate the superior performance of 3W‐MultiHier, achieving 99.8% accuracy with rapid training and low loss. These results highlight the model's efficiency in handling diverse datasets and its potential to improve clinical diagnostics by enabling precise, reliable brain tumor classification.