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

This documentation provides MATLAB source code for breast tumor diagnosis utilizing Learning Vector Quantization (LVQ) neural network classification, along with the required datasets. The program implements an LVQ neural network algorithm that assists medical professionals in achieving more accurate breast tumor classification, thereby improving diagnostic outcomes. Our source code features a carefully designed architecture with optimized network parameters, including proper initialization of prototype vectors and adaptive learning rate mechanisms to ensure both performance and classification accuracy. The implementation includes key MATLAB functions such as lvqnetwork for network creation, lvqtrain for supervised training with labeled data, and lvqclassify for tumor classification. The algorithm employs competitive learning with winner-take-all strategy and distance-based prototype adjustment to separate benign and malignant tumor patterns effectively. We provide comprehensive clinical and experimental datasets obtained through rigorous research, which serve as the foundation for training and testing the classification model. The dataset includes labeled feature vectors containing tumor characteristics such as cell size uniformity, clump thickness, and marginal adhesion measurements. These features are preprocessed and normalized within our code to ensure optimal network performance. By utilizing our source code and datasets, medical practitioners can gain deeper insights into breast tumor characteristics and classification methodologies, enabling them to develop more effective treatment plans and recommendations for patients. The code includes detailed comments and configuration parameters allowing for customization according to specific diagnostic requirements.