• Hybrid CNN-Transformer models achieve superior segmentation accuracy compared to traditional CNNs.
• Data augmentation and transfer learning are critical for overcoming limited medical imaging datasets.
• The proposed method reduces inference time by 30% while maintaining high precision.
• Clinical deployment requires careful consideration of model interpretability and regulatory compliance.