MambaFNO-NET: A Dual-Domain Framework Integrating State Space Models and Fourier Neural Operators for Brain Tumor Segmentation

Ronak Patel¹, Miral Patel², Deep Kothadiya³, Noor A. Khan⁴, Shaha Al-Otaibi^5,*, Roaa Khalil Mohamed Ali Abed⁶, Tanzila Saba⁷
1 U & P U. Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology (CSPIT), Faculty of Technology (FTE), Charotar University of Science and Technology (CHARUSAT), Changa, India
2 G H Patel College of Engineering and Technology, CVM University, V.V. Nagar, Anand, Gujarat, India
3 Symbiosis Centre for Information Technology, Symbiosis International (Deemed University), Pune, India
4 Center of Excellence in Cyber Security (CYBEX), Prince Sultan University, Riyadh, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia
6 College of Sciences and Humanities (CSH), Prince Sultan University, Riyadh, Saudi Arabia
7 AIDA Lab. CCIS, Prince Sultan University, Riyadh, Saudi Arabia
* Corresponding Author: Shaha Al-Otaibi. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.080819

Received 15 February 2026; Accepted 15 April 2026; Published online 05 May 2026

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Abstract

Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of a morphological attention block in the encoder to enhance boundary localization via dilation-erosion gradient modeling; (2) a dual-domain bottleneck module that combines Mamba-inspired sequential modeling with the Fourier Neural Operator (FNO) for efficient local and global pattern modeling with linear complexity; (3) a Feature Pyramid Network (FPN) augmented with Feature-Guided Learning (FGL) for adaptive multi-scale semantic fusion; and (4) Laplacian Pyramid decomposition to preserve high-frequency edge details. The model demonstrates state-of-the-art performance, achieving Dice Similarity Coefficients of 0.81 ± 0.05, 0.92 ± 0.02, and 0.86 ± 0.03 for Enhancing Tumor (ET), Whole Tumor (WT), and Tumor Core (TC) on the BraTS2020 dataset, respectively, with a mean Dice of 0.86 ± 0.05. MambaFNO-NET attains Hausdorff distances (HD95) of 4.21, 6.22, and 6.85 mm for ET, WT, and TC, respectively, resulting in a mean HD95 of 5.76 mm, which underscores its superior boundary localization accuracy. Overall, MambaFNO-NET delivers an efficient and accurate solution for 3D brain tumor segmentation, balancing volumetric precision with spatial boundary alignment for practical clinical deployment.