The invention provides a multi-
modal retinal disease intelligent auxiliary diagnosis
system, which comprises an image preprocessing module used for
processing a binocular
fundus image uploaded by a user; the
disease classification module is used for extracting global features and local
lesion details by adopting a dual-channel DINOv2 model, initializing weights in a
data set through transfer learning, performing fine adjustment on the
fundus image data set, and performing fundus multi-
label classification; and the
batch processing module is used for disassembling batch binocular eye
fundus image processing tasks into independent sub-tasks based on a distributed task scheduling and
dynamic resource allocation technology, and distributing the independent sub-tasks to a plurality of
edge computing nodes for
parallel processing. According to the method, a multi-
label independent classifier is designed, eight independent three-layer MLP classifiers are adopted, and each classifier focuses on single
pathological feature modeling. Through parameter space decoupling design, gradient conflicts among multiple labels are avoided, and accurate capture of heterogeneity pathologies such as diabetes microvascular leakage characteristics and
glaucoma optic cup morphological parameters is ensured.