Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images - Nareekarn Pruthipanyasakul; Nont Kanungsukkasem; Thierry Urruty; Teerapong Leelanupab (My University, 2023)
Integrating AI into medical diagnosis can provide a more accurate diagnosis when medical staff make treatment decisions. This paper studied on several deep neural networks, re-used with further training for a specific task in classifying the stenosis of a patient's coronary artery. From a 4DM-SPECT application, we collected polar map images that report, for example, myocardial perfusion, function and defect severity from cardiac emission computed tomography examination. We conducted a comparative study to identify the optimal combination of various state-of-the-art pre-trained models (i.e., VGG19, ResNet50, DenseNet121, and EfficientNetB0-B3) and eight different modalities of the myocardial perfusion images for classifying the stenosis of the coronary artery.
Keywords: Advanced X-ray and CT Imaging; Cardiac Imaging and Diagnostics; Radiomics and Machine Learning in Medical Imaging