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Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images
Author(s)
Date Issued
January 1, 2023
Type
Conference Paper
Abstract
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.
Citation
2023 15th International Conference on Information Technology and Electrical Engineering Icitee 2023, 311-316, 2023