Diabetic retinopathy (DR) remains a major cause of visual impairment globally, and early, accurate diagnosis is critical for effective intervention. To address the challenges of limited labeled training data, difficulty identifying subtle and dispersed lesions, and redundant feature extraction in retinal images, this paper proposes a DR classification network with a multi-frequency contextual attention module (MFCA-DRNet). First, this paper introduces a self-supervised contrastive learning strategy pre-trained on the EyePACS dataset, eliminating reliance on extensive labeled data and enabling effective feature learning. Next, an adaptive preprocessing method, integrating histogram equalization with non-local means denoising, is designed to enhance image quality by reducing noise and improving lesion visibility. The proposed MFCA module effectively captures long-range contextual relationships and associates dispersed lesion characteristics across retinal images, significantly enhancing lesion recognition. Additionally, the backbone network incorporates an attention mechanism guided by an energy function, emphasizing lesion-specific features while suppressing irrelevant information. Evaluated through downstream classification tasks on DDR, APTOS 2019, and Messidor-2 datasets, MFCA-DRNet achieved strong performance, particularly on the APTOS 2019 dataset, with accuracy, precision, recall, andF1 scores of 87.12%, 81.2%, 85.3%, and 83.16%, respectively. These results highlight MFCA-DRNet's potential to improve DR diagnosis and clinical applicability in diverse imaging conditions.
Liang et al. (Tue,) studied this question.