The invention provides a fundus
macular oedema image processing method based on improved YOLOv11 and DeepSeek, and relates to the technical field of
ophthalmic disease screening, and the method specifically comprises the steps: employing an improved YOLOv11
network structure, adding a lightweight dynamic
convolution module LDConv module and a
convolution block attention module CBAM, dynamically adjusting the shape and size of a
convolution kernel, and carrying out the detection of the fundus
macular oedema image. Multi-scale
lesion characteristics such as
macular edema and exudates are accurately captured; an attention map is generated through a channel and a space attention mechanism, key
lesion areas of
macular edema are preferentially concerned, interference of
background noise is inhibited, and therefore accuracy and robustness of
lesion recognition are further improved; a DeepSeek large
language model is introduced, and on the basis of a detection result, personalized diagnosis and treatment suggestions conforming to clinical guidelines are generated in combination with
clinical information (such as age,
pathogenesis and treatment history) of patients; the
system provides a structured treatment scheme and a follow-up visit plan and assists doctors in making more scientific and personalized decisions.