Work

Integrating state space models and attention mechanisms for brain tumor segmentation in MRI

Year: 2026

Type: article

Abstract: Brain tumor segmentation from MRI is clinically critical yet challenging due to heterogeneous appearance and irregular boundaries. Conventional CNN based methods lack effective global context modeling, while transformer-based approaches are computationally expensive and unstable on limited datasets. To address these, we propose MMA-UNet, a novel hybrid architecture that integrates multiple complementary mechanisms within an encoder-decoder framework. The model employs an EfficientNet-B5 encoder, MedNeXt bridge blocks, a Mamba-based VSS bottleneck, a CBAM enhanced decoder, and deformable refinement for precise boundary adaptation. The model achieves a Dice score of 0.9063, IoU of 0.8304, precision of 0.9043, recall of 0.9092, and specificity of 0.9982 on FigShare benchmark, outperforming the evaluated U-Net, Attention U-Net, TransUNet and Swin UNet under the adopted experimental protocol and maintaining parameter efficiency (30.41 M), demonstrating strong robustness on T1-weighted contrast-enhanced MRI. The proposed model operates on independent 2D slices without volumetric context, and its generalizability to larger datasets remains to be established.

Source: Scientific Reports

DOI: 10.1038/s41598-026-62655-4

Authors: Saritha Saladi, Riyaz Hussain Shaik, Abhi Chevuri, Kishore Penumala, Yepuganti Karuna, Ravi Kumar Mandava

Institutions: VIT-AP University, Rajiv Gandhi University of Knowledge Technologies, Indian Institute of Information Technology Design and Manufacturing, Kurnool

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690