SOM-BP Hybrid Neural Network with MATLAB Implementation for Liver Tumor Image Recognition

This document presents a MATLAB program implementing a SOM-BP hybrid neural network specifically developed for liver tumor image recognition. This carefully designed network architecture combines the strengths of Self-Organizing Maps (SOM) for unsupervised feature extraction and Backpropagation (BP) networks for supervised classification. The implementation includes key MATLAB functions for data preprocessing, network initialization, and training optimization. The program architecture enables efficient analysis of tumor images by first using SOM to cluster and reduce dimensionality of image features, followed by BP network for precise classification. This dual-stage approach significantly improves recognition accuracy of liver tumors. Key implementation features include adaptive learning rates, momentum-based weight updates, and automated feature normalization routines. Through this MATLAB implementation, users can effectively enhance liver tumor recognition rates, providing clinicians with more accurate diagnostic support. The program includes comprehensive error handling, performance visualization tools, and modular code structure for easy customization. Detailed comments explain the algorithmic workflow, including SOM neighborhood function calculations and BP gradient descent optimization. This technical documentation covers both theoretical principles and practical implementation aspects, enabling researchers to understand and apply this hybrid network approach in clinical image analysis. The code includes specialized functions for handling medical image formats, extracting texture features, and validating recognition results against ground truth data.