Optimizing Swin-UNet with Search and Rescue Algorithm for Memory-Efficient Liver Tumor Segmentation on Edge Devices

Liver cancer poses a significant global health challenge, necessitating precise tumor segmentation in CT scans for diagnosis and treatment. While deep learning models like U-Net and Vision Transformers show promise, their computational demands hinder edge deployment. To address this gap, we propose an optimized Swin-UNet framework enhanced by the Search and Rescue (SAR) algorithm, enabling real-time edge computing without compromising the model’s performance. This work proposes a hybrid objective function with quadratic penalties for model compression and area under the curve(AUC). The models are trained using focal AUC loss to mitigate class imbalance. Evaluations on 3DIRCADb, LiTS, and MSD datasets show state-of-the-art performance, with Dice scores of 94.78%, 89.06%, and 88.95%, respectively, and an 80.3% parameter reduction versus baselines. The solution achieves efficient segmentation on edge devices (e.g., Jetson Nano), with a Volume Overlap Error of 1.73% (MSD) and Relative Volume Difference of 0.23% (3DIRCADb), outperforming existing methods. This work advances memory-efficient deep learning for clinical deployment, enabling AI-driven diagnostics in low-resource settings.