Deep Learning Network Optimization Combining 3D Imaging and Multidimensional Signal Processing

This research aims to optimize the deep learning network by combining three-dimensional imaging technology and multidimensional signal processing methods to improve the processing capabilities of complex three-dimensional data. We propose a model based on 3D convolutional neural network (CNN), which is structurally and functionally optimized specifically for 3D image data. In the model design, we introduced an efficient feature extraction mechanism and an improved network training strategy, including batch normalization and regularization techniques, to improve the model's generalization ability and training efficiency. Experimental results show that this model exhibits higher accuracy and robustness than traditional two-dimensional CNN and multi-layer perceptron (MLP) models when processing three-dimensional imaging data. Furthermore, we provide an in-depth analysis of the model's performance and discuss its potential issues and limitations in practical applications. Overall, this study provides an effective deep learning solution for 3D image processing and lays the foundation for future research in a wider range of application fields.