Fusion of Improved Retinex Enhancement and Graph Neural Networks for Retinal Image Quality Classification

The original retinal images are often affected by uneven illumination, noise and other factors, resulting in poor classification accuracy. In this paper, the original retinal image is firstly enhanced by the improved Retinex algorithm, and then the global feature extraction is performed on the enhanced image by using the ResNet-12 model to obtain the global feature vector as the node features, and then the local features of different scales are dynamically adjusted by the multi-scale adaptive aggregation module to highlight the effective information of the nodes. A graph convolutional network is utilized to update the node features, and finally the aggregated node features are input to the fully connected level for classification prediction. Experimental results on two public datasets show that the offered model improves the classification accuracy by 3.12%–15.98%, and is able to more accurately classify retinal images of different quality levels.