GABrain-Net : An Optimized Gabor-Integrated U-Net for Multimodal Brain Tumor MRI Segmentation
Year: 2025
Type: conference-paper
Abstract: Three-dimensional brain tumor MRI segmentation is a challenging task in the field of medical image analysis. Recently, deep learning methods, particularly CNN-based architectures, have significantly improved segmentation by learning complex spatial features. However, challenges such as preserving fine details, texture variation, handling class imbalance, and ensuring generalization persist. Enhancing deep learning models with domain-specific knowledge, such as texture-aware filters, can further improve segmentation accuracy and robustness. This paper presents a novel model that incorporates Gabor convolution into a U-Net architecture to enhance texture analysis and minimize feature loss. The model processes 3D brain tumor MRIs slice by slice, utilizing multi-view inputs to preserve spatial details while maintaining a lightweight design. Furthermore, it investigates the optimal kernel sizes for the Gabor filter, marking the first study to address this crucial aspect of integrating textural analysis with deep learning techniques. Experimental results show that the proposed framework improved segmentation accuracy by using a 7×7 Gabor kernel size and achieving Dice coefficients of 89.78% for WT, 85.60% for TC, and 83.55% for ET, with a mean Dice score of 86.31%. The proposed model demonstrated consistent improvements over the standard U-Net and outperformed several existing state-of-the-art methods.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7124166181