Breast Tumor Diagnosis Using LVQ Neural Network Classification in MATLAB

In the field of medical diagnosis, pattern recognition techniques play a crucial role, and LVQ (Learning Vector Quantization) neural networks are frequently employed for classification tasks due to their efficiency and interpretability. This article explores how to implement benign/malignant breast tumor classification using LVQ neural networks in the MATLAB environment.

### Core Logic of LVQ Neural Network LVQ is a supervised learning neural network model whose fundamental principle involves adjusting the positions of prototype vectors to achieve clear separation between different class samples in the feature space. During training, the algorithm dynamically adjusts the nearest prototype vector based on input sample labels - either attracting prototypes toward same-class samples or repelling them from different-class samples.

### Data Characteristics for Breast Tumor Diagnosis Breast tumor datasets typically contain multiple features (such as tumor thickness, cell size uniformity) and corresponding class labels (benign or malignant). In MATLAB implementation, data usually requires preprocessing: Standardization: Eliminating scale differences between features Train-Test Split: Typically using 70%-30% proportion for partitioning

### Key Implementation Steps in MATLAB Network Initialization: Using `lvqnet` function to specify number of prototype vectors and learning rate Training Configuration: Setting maximum epochs and performance targets Training Process: Implementing iterative optimization using `train` function with input data and labels Performance Validation: Calculating classification accuracy, confusion matrix, and other metrics on test sets

### Optimization Approaches in Practical Applications Feature Selection: Applying PCA or correlation coefficient analysis to identify key features Parameter Tuning: Adjusting learning rate and prototype quantity through cross-validation Result Interpretation: Analyzing decision boundaries by examining final positions of prototype vectors

The advantage of LVQ lies in its lightweight model architecture and intuitive classification results, making it suitable for medical scenarios requiring rapid feedback. However, note that its performance heavily depends on initial prototype vector distribution, which may require multiple experiments to achieve stable results.