Frequency-spatial feature fusion via a hierarchical framework for diabetic retinopathy classification in low-quality fundus images

Low-quality fundus images pose significant challenges for diabetic retinopathy (DR) classification due to noise, blurred boundaries, and the loss of high-frequency details, which hinder both global contextual understanding and local fine-grained feature extraction. To address these limitations, this work proposes a Hierarchical Frequency-Spatial Feature Fusion Network (HFSF-Net) that effectively integrates frequency-domain and spatial-domain information for robust DR classification. Initially, this paper introduces the Spatial Prior-Aware Transformer (SPAT) Block, which incorporates spatial prior knowledge to direct the attention distribution, enabling precise localization of the complex distribution of lesion regions in low-quality fundus images. Subsequently, a novel Wavelet-Enhanced Self-Attention (WESA) module is developed, which utilizes wavelet transforms to extract and enhance high-frequency components such as microvascular textures and edges. Based on WESA, the Wavelet-Enhanced Transformer (WET) Block is constructed to strengthen the ability to recover local details in degraded images. Furthermore, a Hierarchical Frequency-Spatial Fusion (HFSF) module is designed to hierarchically integrate multi-scale features, mitigating information redundancy and resolving feature conflicts between domains. Through this architecture, the model achieves a balanced representation of global and local information. The experiments conducted on the APTOS and DDR datasets yield ACC values of 0.8117 and 0.8021, and Kappa scores of 0.7158 and 0.6731, respectively. Although the model does not achieve exceptionally high Recall, its consistently strong performance in other key metrics supports the claim that the proposed architecture enables a balanced representation of global and local information. Furthermore, the experimental results validate the effectiveness and robustness of HFSF-Net in classifying diabetic retinopathy from low-quality fundus images.