A Diabetic Retinopathy Classification Method Based on Novel Attention Mechanism.

The convolutional neural network (CNN) has been effectively used to do feature extraction in medical image analysis including fundus images for diabetic retinopathy. However, some important detail features are easily ignored, which causes low prediction accuracy. To solve this problem, a CNN feature extraction method based on a novel pixel-level attention mechanism is proposed to achieve an efficient and accurate identification of diabetic retinopathy. Firstly, data normalization and data augmentation are used to improve the quality of datasets. Then, according to the structure characteristics of the two backbone networks Inception- ResnetV2 and EfficientNet-B5, the corresponding attention modules are introduced respectively. Finally, attention networks are trained as feature extractors of fundus images, and complementary deep attention feature descriptors are formed through feature fusion, which can effectively improve the accuracy of the classification model. The experimental results on EyePACS, Messidor and OIA datasets show that the proposed method outperforms the previous ones.