HAAU-Net: Hybrid Adaptive Attention U-Net Integrated with Context-Aware Morphologically Stable Features for Real-Time MRI Brain Tumor Detection and Segmentation.

Overview

1. Department of Electrical and Computer Engineering, Riphah International University, I-14 Campus, Islamabad 44000, Pakistan
2. GUST Engineering & Applied Innovation Research Centre (GEAR), Hawally 32093, Kuwait
3. Department of Engineering, Gulf University for Science and Technology (GUST), Hawally 32093, Kuwait
4. Department of Telecommunication Engineering, University of Engineering and Technology, Taxila 47050, Pakistan

Abstract

Background: The Magnetic Resonance Imaging (MRI)-based tumor segmentation remains a challenging problem in medical imaging due to tumor heterogeneity, unpredictable morphological features, and the high complexity of calculations needed to implement it in clinical practice, putting it out of the scope of real-time applications. Although neural networks have significantly improved segmentation performance, they still struggle to capture morphological tumor features while maintaining computational efficiency. This work introduces Hybrid Adaptive Attention U-Net (HAAU-Net) framework, combining context-aware morphologically stable features and spatial channel attention to achieve high-quality tumor segmentation with less computational cost. Methods: The proposed HAAU-Net framework integrates multi-scale Adaptive Attention Blocks (AAB), Context-Aware Morphological Feature Module (CAMFM) and Spatial-Channel Hybrid Attention Mechanism (SCHAM). CAMFM is used to maintain the stability of morphological features by hierarchical aggregation and dynamic normalization of features. SCHAM enhances feature representation by modelling channels and spatial regions where the strongest feature are determined to use in segmentation. On the BRaTS 2022/2023 data, the proposed HAAU-Net is evaluated using four modalities including T1, T1GD, T2 and T2-FLAIR sequences. Results: The proposed model able to obtain 96.8% segmentation accuracy with a Dice coefficient of 0.89 on the entire tumor region, outperforming the alternative U-Net (0.83) and conventional CNN methods of segmentation (0.81). The proposed HAAU-Net architecture cuts the computational complexity of the standard deep learning models by 43% and still achieve real-time inference (28 FPS on a regular GPU). The hybrid model used to predict survival has a C-Index of 0.91 which is higher than the traditional SVM-based methods (0.72). Conclusions: Spatial-channel attention, combined with morphologically stable features, can be combined to allow clinically significant interpretability in attention maps. The proposed framework significantly improves segmentation performance while maintaining computational effeciency. This broad system has a serious potential of AI-enabled clinical decision support system and early prognostic diagnosis in neuro-oncology with practical deployment capability.

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Data Availability Statement

The implementation code used for training and evaluation of the proposed model is available in a public repository provided by the authors at https://github.com/adeelasghar-cell (accessed on 12 February 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 8 keywords, 7 MeSH terms, 2 funders, 41 references.

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This paper

Asghar, M. A., Shoaib, S., & Zahid, M. (2026). HAAU-Net: Hybrid Adaptive Attention U-Net Integrated with Context-Aware Morphologically Stable Features for Real-Time MRI Brain Tumor Detection and Segmentation. Tomography (Ann Arbor, Mich.), 12(4), 44. https://doi.org/10.3390/tomography12040044

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