Title :
Comparison of four computer-aided diagnosis schemes for automated discrimination of myocardial heart disease
Author_Institution :
Dept. of Radiol. Technol., Niigata Univ., Japan
Abstract :
The aim of this paper is to compare the performance of four different methods, i.e., neural network (NN) with backpropagation learning, NN with genetic-algorithm-based (GA-based) learning, fuzzy reasoning, and the GA-based fuzzy logic approach, for automated discrimination of myocardial heart disease. In our experiments, a total of 90 samples of echocardiographic images from 45 subjects were used. Our results showed that the GA-based fuzzy logic approach is superior to the other three methods. This method enables the classification to achieve a 95.9% of accuracy
Keywords :
backpropagation; echocardiography; fuzzy logic; genetic algorithms; inference mechanisms; medical image processing; neural nets; GA-based fuzzy logic approach; GA-based learning; automated discrimination; backpropagation learning; computer-aided diagnosis schemes; echocardiographic images; fuzzy reasoning; genetic-algorithm-based learning; myocardial heart disease; neural network; Artificial neural networks; Backpropagation; Cardiac disease; Computer aided diagnosis; Electronic mail; Fuzzy logic; Fuzzy reasoning; Heart; Myocardium; Neural networks;
Conference_Titel :
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-5747-7
DOI :
10.1109/ICOSP.2000.893498
