The application discloses a fundus
blood vessel drug application region classification method based on a
time sequence fusion network and relates to the technical field of medical image classification. Step one: after an
image sequence and corresponding labels are acquired, an
image labeling tool is used to convert the image and
label information into a format suitable for
neural network learning, and then a boundary box of a
drug application region is drawn on the last image and the corresponding category is labeled; the fundus
blood vessel drug application region classification method based on the
time sequence fusion network, the application considers the characteristics of FFA image
drug application region classification and proposes a
time sequence fusion target detection network, a group of FFA images are input, the time sequence fusion target detection network can not only locate the
drug application region but also extract time sequence information of the
drug application region to provide a basis for classification. This not only makes up for the deficiency that a traditional target detection network cannot process sequence images, but also introduces a time sequence
information extraction module to simulate an observation process of dynamic changes of a drug application region in an artificial classification process, so that the classification accuracy can be improved.