QCNN-Swin-UNet: Quantum Convolutional Neural Network Integrated with Optimized Swin-UNet for Efficient Liver Tumor Segmentation and Classification on Edge Devices.

Accurate segmentation and classification of liver tumors are crucial for early diagnosis and effective treatment planning. However, conventional deep learning models such as tumor heterogeneity, class imbalance, and high computational demands face challenges, limiting their clinical deployment. This study introduces a lightweight hybrid framework combining an optimized Swin-UNet for segmentation with a Quantum Convolutional Neural Network (QCNN) for classification. The Swin-UNet is enhanced using a metaheuristic Search and Rescue (SAR) algorithm and a quadratic penalty-based objective function to balance compactness and accuracy. A Focal AUC loss function addresses class imbalance and improves sensitivity to minority regions. The QCNN leverages quantum-inspired mechanisms such as entanglement and superposition to achieve superior performance with reduced parameters. Evaluated on three benchmark datasets (3D-IRCADb, LiTS17, and MSD Task03), the framework achieves Dice scores of 85.8%, 88.7%, and 88.4%, respectively, alongside 96.7% classification accuracy. The model size is reduced to 64.16 MB, enabling real-time inference on edge devices (Jetson Nano). The QCNN classifier outperforms traditional CNNs in all metrics, demonstrating its effectiveness in high-dimensional medical data analysis. This work bridges the gap between diagnostic precision and computational efficiency, presenting a clinically viable AI solution for liver tumor analysis.