心筋症における超音波画像を用いた支援診断システムの性能比較評価

概要

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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.
公益社団法人日本放射線技術学会の論文
2000-03-20