FFLUNet: Feature Fused Lightweight UNet for brain tumor segmentation.

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Abstract

Brain tumors, particularly glioblastoma multiforme, are considered one of the most threatening types of tumors in neuro-oncology. Segmenting brain tumors is a crucial part of medical imaging. It plays a key role in diagnosing conditions, planning treatments, and keeping track of patients' progress. This paper presents a novel lightweight deep convolutional neural network (CNN) model specifically designed for accurate and efficient brain tumor segmentation from magnetic resonance imaging (MRI) scans. Our model leverages a streamlined architecture that reduces computational complexity while maintaining high segmentation accuracy. We have introduced several novel approaches, including optimized convolutional layers that capture both local and global features with minimal parameters. A layerwise adaptive weighting feature fusion technique is implemented that enhances comprehensive feature representation. By incorporating shifted windowing, the model achieves better generalization across data variations. Dynamic weighting is introduced in skip connections that allows backpropagation to determine the ideal balance between semantic and positional features. To evaluate our approach, we conducted experiments on publicly available MRI datasets and compared our model against state-of-the-art segmentation methods. Our lightweight model has an efficient architecture with 1.45 million parameters - 95% fewer than nnUNet (30.78M), 91% fewer than standard UNet (16.21M), and 85% fewer than a lightweight hybrid CNN-transformer network (Liu et al., 2024) (9.9M). Coupled with a 4.9× faster GPU inference time (0.904 ± 0.002 s vs. nnUNet's 4.416 ± 0.004 s), the design enables real-time deployment on resource-constrained devices while maintaining competitive segmentation accuracy. Code is available at: FFLUNet.

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