Enhancement of CAD System for Breast Cancers by Improvement of Classifiers

摘要

In this paper, we propose a method to improve the accuracy of classifiers by replacing the connection between the output layer and the immediately preceding hidden layer with an optimal linear transformer. This approach is intended to improve the performance of a breast cancer image diagnosis assistance system. The proposed classifier is composed of a three-layer MLP (multilayer perceptron) and a Mahalanobis classifier. The MLP has only one output unit, and produces output for two categories. If it is assumed that the value from the hidden layer immediately preceding the output layer forms a multivariable normal distribution for each class, that is, a Gaussian distribution, then the optimal linear transformer is a classifier based on the generalized Mahalanobis distance. Thus, the optimal classification is realized in the MLP after learning in which the generalized Mahalanobis distance with the hidden layer immediately preceding the output layer as the input is examined, and classification is performed on the basis of the likelihood. The proposed breast cancer image diagnosis assistance system, the system using only the conventional Mahalanobis classifier, and the system using only the conventional MLP classifier are compared. The best results are given by the proposed method, and it is shown that the performance of the breast image diagnosis assistance system can be improved.