Coronary artery disease (CAD) is one of the leading causes of death in the world, especially in the middle-aged and old populations. This study investigated the efficiency of the diagnoses of coronary artery disease by a deep-learning model using polar maps and slice images derived from myocardial perfusion imaging (MPI) by single photon emission computed tomography (SPECT) cameras. Data for evaluation were collected at the Department of Nuclear Medicine, 108 Military Central Hospital. The experimental results showed that learning from myocardial perfusion imagingI slice images provided a higher diagnosis accuracy than from polar map images. However, there still have space for improving the accuracy of detecting CAD. Our future work is to improve the performance of the CAD detection.
