A hybrid CNN-GNN and multitask learning pipeline for improving mild diabetic retinopathy sensitivity

Diabetic Retinopathy (DR) is a progressive microvascular complication of diabetes that can lead to irreversible vision loss if early-stage abnormalities remain undetected. Mild DR is particularly challenging to identify due to subtle micro-lesions, severe class imbalance, and limited robustness of conventional deep learning models under domain shift. To address these challenges, this article introduces a hybrid CNN-GNN multitask pipeline designed to improve sensitivity toward Mild DR. The proposed method integrates CNN for deep feature extraction, followed by graph-based representations that explicitly model spatial relationships between retinal regions. Both grid-based and adaptive k-nearest neighbor graph construction strategies are supported. A graph neural network performs message passing on these representations, and multitask learning is applied using two prediction heads: multiclass DR grading and binary Mild vs Non-Mild classification.•The proposed Hybrid CNN-GNN Multitask Pipeline improves sensitivity toward Mild Diabetic Retinopathy by integrating convolutional feature extraction with graph-based spatial modelling.•Multitask supervision enables enhanced Mild DR detection without substantial trade-off in overall classification accuracy, supporting balanced screening performance.•Validation across in-domain and cross-domain scenarios demonstrates the method's ability to consistently prioritize Mild DR detection, with evaluation protocol provided.