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Brain tumor segmentation using dual-stream multiscale 3D-UNET with dense net and spatial attention.
Deema Mohammed AlSekait1, Mohammed Zakariah2, Parul Dubey3
1Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific Reports
|April 1, 2026
Summary
This study introduces an advanced Dual-stream 3D-UNET for brain tumor segmentation in MRI scans. The model achieves high accuracy in identifying tumor subregions, improving diagnostic capabilities.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neuroscience
Background:
- Accurate brain tumor segmentation in Magnetic Resonance Imaging (MRI) is critical for diagnosis and treatment planning.
- Challenges include variable tumor appearance and precise subregion delineation.
- Existing models require updates for standardized datasets like BRATS 2020.
Purpose of the Study:
- To develop an improved brain tumor segmentation approach using a novel network architecture.
- To enhance segmentation accuracy for various tumor subregions (edema, enhancing tumors, core tumors).
- To validate the model's performance on the BRATS 2020 and BRATS 2021 datasets.
Main Methods:
- A Dual-stream multi-scale 3D-UNET network was developed, integrating DenseNet and spatial attention mechanisms.
- The model processes multichannel MRI data, extracting features and focusing on relevant areas.
- Segmentation was performed on the BRATS 2020 dataset, focusing on seven tumor categories.
Main Results:
- The proposed model achieved high Dice scores: 99.12% (enhancing tumors), 99.99% (swelling/edema), and 99.88% (core tumors) on BRATS 2020.
- Overall Dice score reached 99.99% on the BRATS 2020 dataset.
- Validation on BRATS 2021 showed accuracies of 99.45% (enhancing tumors), 98.24% (swelling/edema), and 99.55% (core tumors).
Conclusions:
- The Dual-stream multi-scale 3D-UNET with DenseNet and spatial attention significantly improves brain tumor segmentation accuracy.
- The model demonstrates robust performance on benchmark datasets, outperforming baseline methods.
- Future work includes real-time adaptation, unsupervised learning generalization, and clinical interpretability enhancement.