Abstract
Retinal image quality assessment (RIQA) is crucial for diagnosing various eye diseases and ensuring the accuracy of diagnostic analyses based on retinal fundus images. Traditional deep convolutional neural networks (CNNs) for RIQA face challenges such as over-reliance on RGB image brightness and difficulty in differentiating closely ranked image quality categories. To address these issues, we introduced the Dual-Path Frequency-domain Cross-attention Network (DFC-Net), which integrates RGB images and contrast-enhanced images using contrast-limited adaptive histogram equalization (CLAHE) as dual inputs. This approach improves structure detail detection and feature extraction. We also incorporated a frequency-domain attention mechanism (FDAM) to focus selectively on frequency components indicative of quality degradations and a cross-attention mechanism (CAM) to optimize the integration of dual inputs. Our experiments on the EyeQ and RIQA-RFMiD datasets demonstrated significant improvements, achieving a precision of 0.8895, recall of 0.8923, F1-score of 0.8909, and a Kappa score of 0.9191 on the EyeQ dataset. On the RIQA-RFMiD dataset, the precision was 0.702, recall 0.6729, F1-score 0.6869, and Kappa score 0.7210, outperforming current state-of-the-art approaches.