The invention relates to the technical field of brain imaging, in particular to a
microwave imaging detection method and device based on a fusion
algorithm,
electronic equipment and a medium, and the method comprises the steps: S1, collecting actual
microwave scattering
electric field distribution; s2, inputting the acquired actual
microwave scattering
electric field distribution into a Born
approximation algorithm model, and performing inversion to obtain a first brain
foreign body initial imaging result; s3, inputting the first brain
foreign body initial imaging result into a
deep learning neural
network model to obtain first
microwave scattering electric field distribution; s4, inputting the first
microwave scattering electric field distribution into the Born
approximation algorithm model again to continue iterative calculation to obtain a second brain
foreign body initial imaging result, and inputting the second brain foreign body initial imaging result into the
deep learning neural
network model again to obtain second
microwave scattering electric field distribution; s5, the microwave scattering electric field distribution is continuously iteratively updated in the step S4 until the difference value between the updated microwave scattering electric field distribution and the actual microwave scattering electric field distribution is smaller than a preset threshold value, and at the moment, the brain foreign body initial imaging result corresponding to the updated microwave scattering electric field distribution is a high-resolution brain foreign body imaging result; and S6, further judging whether the brain foreign body exists or not according to the high-resolution brain foreign body imaging result. According to the method, the microwave
signal features are automatically extracted by using the
deep learning model, and fine physical modeling is performed through the Born iteration method, so that the accuracy and reliability of detection are remarkably improved.