The application discloses a
choroid neovascularization image segmentation method based on a
hybrid convolutional network, first, collecting fundus OCT scan images and labeling, and forming a
data set by using the labeled images; a deep space-time separation
hybrid convolutional neural network fusing an attention mechanism is constructed, two-dimensional
feature extraction is carried out by using two-dimensional
convolution, then the two-dimensional
feature extraction is extended to three-dimensional, and then three-dimensional attention deep space-time separation
convolution is carried out, and the two-dimensional feature and the three-dimensional feature are aligned and fused; then the
hybrid neural
network model constructed is trained by using the
data set; finally, the trained
hybrid neural network model is used to segment
choroid neovascularization from the fundus OCT scan images, and a segmentation result is obtained. Through the space-time attention mechanism, local features of the
choroid neovascularization image are better extracted, and by using the deep space-time separation
convolution, a dimension reduction operation is carried out on the input feature map, so that the calculation parameters can be effectively reduced, thereby reducing the network calculation amount, and the channel attention can be more effectively calculated.