Classification Method of Micro-Calcifications Based on LVQ Neural Networks
Zhong Ming-xia · Computer Era · 2011
A classification method of benign and malignant breast tumors based on adaptive LVQ(Learning Vector Quantization) neural networks is proposed,on the basis of extracting feature vectors,it trains and tests benign and malignant digitized mammograms of both CC and MLO views,and analyzes the classification results by using optimal and average classification rates.The experiment results show that the average test classification rate of the method is 92.6% for CC view images,93.18% for MLO view.In micro-calcification classification system,if logic OR is used to merge the networks under the two different views,the best classification performance that can be achieved is 94.8%.