MATLAB Source Code for Breast Tumor Diagnosis Using LVQ Neural Network Classification

Breast tumor diagnosis using LVQ neural network represents an efficient pattern recognition application that can differentiate between benign and malignant tumors by learning sample characteristics.

LVQ Neural Network Overview Learning Vector Quantization (LVQ) is a supervised competitive learning neural network commonly used for classification tasks. It adjusts prototype vectors to establish boundaries between different classes, making it suitable for binary or multi-class classification problems in medical diagnosis. In breast tumor diagnosis, LVQ learns the relationship between tumor features (such as cell size, morphology) and malignancy labels to achieve automated classification.

Implementation Workflow Data Preprocessing: Breast tumor datasets typically contain multiple features (like tumor thickness, cell uniformity). Normalization or standardization is required to improve model convergence speed. Network Construction: Initialize prototype vectors: Random selection or generation based on sample means. Set learning rate and iteration count: Controls update step size and training epochs. Training Phase: Compare input samples with nearest prototype vectors, adjusting prototype positions (moving closer or farther from samples) based on class agreement. Classification Prediction: Test samples are assigned to the nearest class by calculating distances to prototype vectors.

MATLAB Implementation Key Points Use the `newlvq` function to create LVQ network, specifying input dimensions, number of hidden layer nodes, and class proportions. Training function `train` optimizes prototype vectors through backpropagation. Use `sim` function for classification to output predicted labels.

Application Advantages Strong interpretability: Prototype vectors visually represent classification boundaries. Computational efficiency: Suitable for small to medium-scale medical datasets.

Extension Directions Combine with feature selection methods (like PCA) to optimize input dimensions. Integrate with other classifiers (such as SVM) to enhance robustness.

(Note: Ensure data compliance in practical applications and validate model generalization capability.)