The invention discloses a
thrombolysis prediction method based on
deep learning and multi-
modal fusion, and belongs to the technical field of
thrombolysis prediction, and the method comprises the steps: S1, collecting an
MRI image of a patient with ischemic cerebral apoplexy, and constructing a cerebral apoplexy
automatic segmentation model; s2, carrying out
image registration; s3,
lesion segmentation; and S4,
thrombolysis prediction. According to the cerebral apoplexy
automatic segmentation model based on the gating parallel Mama network, the long-distance dependency relationship in the image can be effectively captured, ischemic cerebral apoplexy lesions can be rapidly and accurately segmented, and the
lesion identification efficiency and accuracy are greatly improved; according to the
algorithm provided by the invention, the linear
time complexity is realized, the computing
resource consumption is greatly reduced, and the
bottleneck of the prior art is broken through; according to the cerebral apoplexy thrombolysis prediction model based on the multi-
modal data, scientific automatic decision support is provided for judging whether the patient is suitable for thrombolysis treatment or not, and the quality and safety of medical decision are improved.