Neural Network Approach for the Computer-Aided Diagnosis of Coronary Artery Diseases in Myocardial SPECT Bull’s-Eye Images

We have developed a computerized scheme that can aid in the radiologists diagnosis in the detection and classification of coronary artery diseases in 201T1 myocardial SPECT (single photon emission computed tomography) bull’s-eye images by use of artificial neural networks. The multi-layer feed-forward neural networks used with a back-propagation algorithm have 41–256 input units (pattern: compressed images), 50 or 100 units in a single hidden layer, and eight output units (diagnosis: one normal and seven different types of abnormalities). The neural networks consisting of two major networks for “EXTENT” and “SEVERITY” images were trained using pairs of training input data (bull’s-eye image) and desired output data (“correct” diagnosis). The results show that the recognition performance of our neural-network-based system is comparable to that of two year experienced radiologists. Our study suggests that the neural network approach is useful for the computer-aided diagnosis of coronary artery diseases in myocardial SPECT bull’s-eye images.