A Tumor Aware DenseNet Swin Hybrid Learning with Boosted and Hierarchical Feature Spaces for Large-Scale Brain MRI Classification

TL;DR

This paper introduces a novel hybrid DenseNet Swin architecture for large-scale brain MRI tumor classification, combining local texture and global shape features to improve diagnostic accuracy and reduce false negatives.

Contribution

It proposes a dual-setup framework with boosted and hierarchical feature learning, integrating DenseNet and Swin Transformer for enhanced brain tumor MRI analysis.

Findings

Achieved 98.50% accuracy on large MRI dataset

Outperformed standalone CNNs, Transformers, and other hybrids

Effectively reduced false negatives in tumor classification

Abstract

This study proposes an efficient Densely Swin Hybrid (EDSH) framework for brain tumor MRI analysis, designed to jointly capture fine grained texture patterns and long range contextual dependencies. Two tumor aware experimental setups are introduced to address class-specific diagnostic challenges. The first setup employs a Boosted Feature Space (BFS), where independently customized DenseNet and Swint branches learn complementary local and global representations that are dimension aligned, fused, and boosted, enabling highly sensitive detection of diffuse glioma patterns by successfully learning the features of irregular shape, poorly defined mass, and heterogeneous texture. The second setup adopts a hierarchical DenseNet Swint architecture with Deep Feature Extraction have Dual Residual connections (DFE and DR), in which DenseNet serves as a stem CNN for structured local feature learning, while Swin_t models global tumor morphology, effectively suppressing false negatives in meningioma and pituitary tumor classification by learning the features of well defined mass, location (outside brain) and enlargments in tumors (dural tail or upward extension). DenseNet is customized at the input level to match MRI spatial characteristics, leveraging dense residual connectivity to preserve texture information and mitigate vanishing-gradient effects. In parallel, Swint is tailored through task aligned patch embedding and shifted-window self attention to efficiently capture hierarchical global dependencies. Extensive evaluation on a large-scale MRI dataset (stringent 40,260 images across four tumor classes) demonstrates consistent superiority over standalone CNNs, Vision Transformers, and hybrids, achieving 98.50 accuracy and recall on the test unseen dataset.