A neuro-fuzzy algorithm for diagnosis of coronary artery stenosis.
In this paper a method of fuzzy decision making applied to diagnosis of coronary artery stenosis is presented. The method uses a neural network approach for the diagnosis of stenosis in the three main coronary arteries (left anterior descending, right coronary artery, and circumflex). First, the knowledge base domain, 201Tl scintigram training data, is explained and the method of preprocessing the original heart images is given. Next, the method of dealing with the uncertainties present in the data using the fuzzy approach is outlined. Finally, the algorithm and the results are discussed and compared with other approaches.
Sztandera
L M
LM
Department of Computer Science, Philadelphia College of Textiles and Science, PA 19144, USA.
Goodenday
L S
LS
Cios
K J
KJ
D000465:Algorithms; D016022:Case-Control Studies; D003327:Coronary Disease / Q000000981:diagnostic imaging*; D005080:Exercise Test; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D011877:Radionuclide Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013794:Thallium Radioisotopes
D013794:Thallium Radioisotopes