The invention relates to the technical field of
image processing, and particularly discloses a
diabetic retinopathy fine-grained grading-oriented self-adaptive multi-
scale model, which comprises a layered global context module for fusing multi-scale
feature extraction, self-adaptive attention enhancement and dynamic weighted fusion, constructing a global and local information complementary multi-scale representation mechanism, and constructing a multi-scale
feature extraction model; feature representation with higher expressive power is generated; the multi-scale adaptive attention module captures a complex dependency relationship among different spatial positions in the feature map through a synergistic effect of multi-scale
feature extraction and adaptive attention enhancement; the relation multi-head attention module is used for deeply mining and capturing complex association among different features in a feature map so as to enhance the identification capability and classification performance of the model on fine-grained
lesion features; and the attention mechanism module is used for capturing a key channel and an important space region in the feature map by combining a channel and a space attention mechanism so as to comprehensively and finely analyze diversified features of DR lesions.