Hybrid GELAN-UNet: integrating medical priors for low-dose CT denoising

Low-Dose Computed Tomography (LDCT) reduces radiation risk but introduces high noise levels that compromises diagnostic quality. To address this, we propose a Hybrid Generalized Efficient Layer Aggregation Network-UNet (GELAN-UNet) model, which incorporates medical priors into a progressive modular architecture. This design uses medically enhanced modules in shallower layers to capture fine details and computationally efficient blocks in deeper layers to reduce cost. Key innovations include a novel low-frequency retention path and an edge-aware attention mechanism, both crucial for preserving critical diagnostic structures. Evaluated on the public Mayo Clinic dataset, the proposed method achieves a superior peak signal-to-noise ratio (PSNR) of 45.28 dB - a 12.45% improvement over the original LDCT - while maintaining an optimal balance between denoising performance and computational efficiency. The critical importance of the low-frequency path, as revealed by ablation studies, validates the rationality of the hybrid strategy, which is further supported by comparisons with full medical and frequency-aware variants. This work delivers a high-performance denoising model alongside a practical, efficient architectural paradigm - rigorously validated through systematic exploration - for integrating domain-specific medical knowledge into deep learning frameworks.