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The present study represents an attempt to improve the separation of specific high-risk coronary stenosis from lower-risk conditions by employing an image-recognition neural network. The system mimics the visual reading of scintigraphs from raw digitized data, with the added benefit of a computerized classification system. It models the human retina as the sensing organ that processes the image signals and forwards them to the brain, where the outputs from each visual segment are processed to produce a recognition code. In the application described here, the recognition code classifies a scintigraphic image as demonstrating normal myocardial perfusion, or a perfusion pattern consistent with single-vessel, multiple-vessel, or left-anterior descending coronary artery stenosis. The input images are from clinically performed postexercise planar myocardial perfusion scintigraphs as produced in many clinical laboratories.