Classifying retinal images via vascular-optic disc cross-segmentation and attentive feature selection.

The prevalence of retinal disorders is rising at an alarming rate, posing a significant risk of irreversible blindness without timely intervention. Recent advancements in artificial intelligence (AI) have enabled the development of automated screening systems for retinal image analysis. However, building robust models presents challenges due to inconsistencies in image acquisition, varying resolutions, and imbalanced class distributions. To address these issues, this research introduces a novel abnormality-aware attentive feature selection approach for classifying retinal images into three categories: healthy (H), glaucoma (G), and diabetic retinopathy (DR). This method focuses on extracting the most discriminative features to enhance classification performance. Additionally, we propose a cross-segmentation framework that extracts and integrates comprehensive multiview information-specifically, optic disc and vascular structures-from the input retinal images. By leveraging these supplementary features, our approach effectively addresses low inter-class variability and enriches the information available for accurate classification. Extensive experiments conducted on diverse publicly available datasets-including FIVES, Drishti-GS1, SUSTech, HRF, and PAPILA-demonstrate the robustness of the proposed method. The model achieved a balanced accuracy of 83% (95% CI: 80%-86%), with sensitivities of 0.81 for the healthy class, 0.81 for glaucoma, and 0.87 for diabetic retinopathy. The corresponding specificity values were 0.84, 0.83, and 0.85; positive predictive values (PPV) were 0.83, 0.75, and 0.90; and negative predictive values (NPV) were 0.86, 0.91, and 0.96, respectively. These results indicate that the model is robust in distinguishing between the three classes, exhibiting balanced performance across all metrics.