| The aim of this study was to compare the performance of four different computer-aided diagnosis systems, i.e., a neural network(NN)with back-propagation(BP)learning, an NN with genetic-algorithm(GA)-based learning, a fuzzy logic method, and a GA-based fuzzy logic approach, for automated discrimination of myocardial heart disease. We evaluated the performance of the four systems in terms of accuracy, sensitivity, and specificity. In our experiments, a total of 90 echocardiographic images from 45 subjects(an end-diastole image and an end-systole image from each subject)were used. Four statistical features, namely, angular second moment, contrast, correlation, and entropy were extracted from each composite image obtained from the corresponding end-diastole and end-systole images. These features were subsequently used in our classification schemes. Our results showed that the GA-based fuzzy logic method was superior to the other three methods. This method enabled the classification to achieve 95.9% of the average recognition rate. Thus the GA-based fuzzy logic approach is considered to be a promising tool for the discrimination of myocardial heart disease. |
