The invention relates to a structure multi-
disease identification method based on a lightweight full
convolution residual network, and the method comprises the following steps: obtaining an image of a single
disease, setting the size of the image, marking each image sample, and generating a single
disease data set; preprocessing images in the single disease
data set, generating a compound disease
sample image in which multiple diseases coexist through cross
aliasing, and constructing a multi-disease
data set; building a full
convolution residual
network model, setting hyper-parameters, and training the network by adopting an Adam optimization
algorithm and a classification
cross entropy loss function; and carrying out structure disease identification by using the trained full
convolution residual network, thereby realizing simultaneous, rapid and accurate identification of multiple diseases. Compared with the prior art, various diseases can be efficiently identified under the condition that various diseases coexist, the limitation that a traditional method can only identify a single disease is overcome, and the method has remarkable practical value and a good application effect.