A diagnosis aid model for acute ischemic stroke, and an image processing method, relating to the technical field of medical image processing. The model is a generative adversarial network model (1), and comprises a first three-dimensional convolutional neural network as a generator G (2) and a second three-dimensional convolutional neural network as a discriminator D (3); the generator G (2) is used for completing image-to-image conversion; the discriminator D (3) is used for determining the authenticity of an input image. The generative adversarial network model (1) can learn conversion relationships from non-enhanced computed tomography (NECT) images to T2-weighted fluid-attenuation inversion recovery (FLAIR) magnetic resonance images; the use of the trained generative adversarial network model (1) having completed learning makes, during stroke diagnosis, a doctor only need to scan a brain NECT image to generate a FLAIR image by means of the model to assist in quick stroke diagnosis, thereby improving the efficiency of stroke screening in first aid, and overcoming the current clinical dilemmas that the sensitivity of NECT images is not high and MRI images are difficult to acquire in time.
DIAGNOSIS AID MODEL FOR ACUTE ISCHEMIC STROKE, AND IMAGE PROCESSING METHOD