Patent ID: 11875488
Assignee: HENAN UNIVERSITY OF TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 1:
2. The method of claim 1, wherein 2) is implemented as follows:
2.1) preliminarily generating the virtual retinal image from the real retinal image by geometric transformation;
inputting the real retinal image RImi, and applying a geometric transformation operator Tg to the real retinal image to obtain the virtual retinal image SImj, i=1, 2, . . . , n, j=1, 2, . . . , m:

SIMj=Tg(RImi)  (7)

where Tg is one of flip, clip, rotate, translate geometric transformation operators or a composition operator thereof;
2.2) generating, according to a preliminarily generated virtual retinal image and the real retinal image, a new virtual retinal image by using a generative adversarial network, where a discriminator for the generative adversarial network consists of a decomposed fuzzy broad convolutional neural network and a generator for the generative adversarial network consists of a plurality of interval type-2 fuzzy convolutional neural networks and/or multi-layer interval type-2 fuzzy neural network;
training the generator by using different objective functions as training objectives to obtain a plurality of virtual retinal images, and selecting a finally generated virtual image from the plurality of virtual retinal images according to the following criterion:

∥μr−μg∥p+tr(Cr+Cg−2(CrCg)1/2)  (11)

where μr and μg, Cr and Cg denote, respectively, a sample mean vector and covariance matrix of a probability distribution pdata of real retinal image samples and a probability distribution pg of virtual retinal image samples synthesized by the generator; ∥⋅∥p denotes the p vector norm, and tr(⋅) denotes the trace of the matrix; and
obtaining the virtual retinal image when Formula (11) is minimized in a generated virtual retinal image.