A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

Diabetic retinopathy (DR), the main cause of irreversible blindness, is one of the most common complications of diabetes. At present, deep convolutional neural networks have achieved promising performance in automatic DR detection tasks. The convolution operation of methods is a local cross-correlation operation, whose receptive field determines the size of the local neighbourhood for processing. However, for retinal fundus photographs, there is not only the local information but also long-distance dependence between the lesion features (e.g. hemorrhages and exudates) scattered throughout the whole image. The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection. Patch-wise relationships are used to enhance the local patch features since lesions of DR usually appear as plaques. The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks. Extensive experimental results demonstrate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.