Medical Image Segmentation Using a Global Context-Aware and Progressive Channel-Split Fusion U-Net with Integrated Attention Mechanisms
Abstract
Medical image segmentation serves as a key component in Computer-Aided Diagnosis (CAD) systems across various imaging modalities. However, the task remains challenging because many images have low contrast and high lesion variability, and many clinical environments require efficient models. This study proposes CFCSE-Net, a U-Net-based model that builds upon X-UNet as a baseline for the CFGC and CSPF modules. This model incorporates a modified CFGC module with added Ghost Modules in the encoder, a CSPF module in the decoder, and Enhanced Parallel Attention (EPA) in the skip connections. The main contribution of this paper is the design of a lightweight architecture that combines multi-scale feature extraction with an attention mechanism to maintain low model complexity and increase segmentation accuracy. We train and evaluate CFCSE-Net on four public datasets: Kvasir-SEG, CVC-ClinicDB, BUSI (resized to 256 × 256 pixels), and PH2 (resized to 320 × 320 pixels), with data augmentation applied. We report segmentation performance as the mean ± standard deviation of IoU, DSC, and accuracy across three random seeds. CFCSE-Net achieves 79.78% ± 1.99 IoU, 87.21% ± 1.72 DSC, and 96.70% ± 0.59 accuracy on Kvasir-SEG, 88.11% ± 0.86 IoU, 93.42% ± 0.55 DSC, and 99.04% ± 0.09 accuracy on CVC-ClinicDB, 69.33% ± 2.66 IoU, 78.80% ± 2.65 DSC, and 96.30% ± 0.51 accuracy on BUSI, and 92.27% ± 0.52 IoU, 95.92% ± 0.30 DSC, and 98.06% ± 0.16 accuracy on PH2. Despite its strong performance, the model remains compact with 909,901 parameters and low computational cost, requiring 3.24 GFLOPs for 256 × 256 inputs and 5.07 GFLOPs for 320 × 320 inputs. These results show that CFCSE-Net maintains stable performance on polyp, breast ultrasound, and skin lesion segmentation while it stays compact enough for CAD systems on hardware with low computational resources.
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