A Lightweight Swin-UNet Model for Accurate Liver Tumor Segmentation on Memory-Constrained Devices

Liver cancer remains a critical global health challenge, with accurate segmentation of tumors in CT scans being vital for diagnosis. While deep learning models like U-Net and Vision Transformers offer promising results, their high computational demands hinder deployment on resource-constrained edge devices. This study introduces an optimized Swin-UNet model for efficient liver tumor segmentation, leveraging a Search and Rescue (SAR) algorithm to balance model size and accuracy. Key innovations include a quadratic penalty-based objective function for joint optimization of AUC and model compactness and a focal AUC loss to address class imbalance. Evaluated across three datasets (3DIRCADb, LiTS, MSD), the proposed SAR-Swin-UNet achieves superior performance, with Dice scores of 94.78%, 89.06% and 88.95%, respectively, while reducing the model size by up to 80.3% compared to unoptimized counterpart. The approach enables real-time, energy-efficient segmentation on edge devices like Jetson Nano, addressing challenges of data security and computational costs. Results demonstrate significant improvements over state-of-the-art methods with a minimum Volume Overlap Error of (1.73%) in MSD dataset and Relative Volume Difference of (0.23%) in 3DIRCADb dataset, highlighting its clinical applicability for precise, low-resource settings. This work bridges the gap between high-accuracy segmentation and practical deployment in under-resourced healthcare environments.