Fuzzy-reasoning-based computer-aided diagnosis for automated discrimination of myocardial heart disease from ultrasonic images

This paper describes a fuzzy-reasoning-based com-puter-aided diagnosis scheme for automated classification of cardiomyopathy from ultrasonic images. Unlike the con-ventional types of membership functions such as triangle and trapezoid, Gaussian-distributed membership functions (GDMFs) are employed in the present study. The GDMFs are initially generated using various texture-based features computed from gray-level co-occurrence matrices. Sub-sequently, the shapes of GDMFs are optimized by a genetic-algorithm learning process. After optimization, the classifier is used to discriminate two sets of echocardiog-raphic images, namely, normal and abnormal cases, which were diagnosed in advance by a highly trained physician. We experimentally evaluate the performance of the pro-posed method against various methods reported in terms of sensitivity, specificity, and total accuracy. Our results show the promise of the proposed approach in discriminatin