Fuzzy-reasoning-based diagnosis scheme for automated classification of heart disease from ultrasonic images

This paper presents a fuzzy-reasoning-based computer-aided diagnosis scheme for automated classification of heart disease from ultrasonic images. Unlike the conventional types of membership functions, 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. Subsequently, the shapes of GDMFs are optimized by the genetic-algorithm learning process. After optimization, the classifier is used to discriminate two sets of echocardiographic images, namely, normal and abnormal cases, which were diagnosed in advance by a highly trained physician. We experimently evaluate the performance of the proposed method against various methods reported in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed method has potential utility for computer-aided diagnosis of myocardial heart disease.