Open Access

ARTICLE

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:

(This article belongs to the Special Issue: Advanced Image Segmentation and Object Detection: Innovations, Challenges, and Applications)

Computer Modeling in Engineering & Sciences 2026, 147(2), 47 https://doi.org/10.32604/cmes.2026.080819

Received 15 February 2026; Accepted 15 April 2026; Issue published 27 May 2026

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.

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