A deep integrative approach for diabetic retinopathy classification with synergistic channel-spatial and self-attention mechanism

Diabetic retinopathy (DR) is a prevalent eye disease that poses a significant risk of vision loss in individuals with diabetes. Accurate classification of DR is crucial for timely intervention and effective treatment. However, the classification task is challenging due to imbalanced datasets, small lesions, and inter-class similarity. This study proposes a novel deep integrative approach for DR classification, leveraging the strengths of residual blocks, channel-spatial attention mechanism (CSAM), and non-local blocks (NLB). The proposed architecture consists of a sequence of residual blocks followed by CSAM modules, which enhance feature discriminability at different scales in DR images. Additionally, the outputs from CSAM blocks are fed into an NLB to capture long-range dependencies, allowing the model to consider global context information. The proposed architecture primarily utilizes the strength of global features extracted from non-local blocks and fuses them to enrich the information representation. Experimental results demonstrate that the proposed method effectively addresses challenges in DR classification and shows superior performance in terms of computational time and improved accuracy compared to existing methods.