Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images

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dc.contributor.authorPruthipanyasakul, Nareekarn
dc.contributor.authorKanungsukkasem, Nont
dc.contributor.authorUrruty, Thierry
dc.contributor.authorLeelanupab, Teerapong
dc.date.accessioned2026-08-06T10:38:56Z
dc.date.available2026-08-06T10:38:56Z
dc.date.issued2023-01-01
dc.description.abstractIntegrating 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.
dc.identifier.citation2023 15th International Conference on Information Technology and Electrical Engineering Icitee 2023, 311-316, 2023
dc.identifier.doi10.1109/ICITEE59582.2023.10317700
dc.identifier.other2-s2.0-85179884838
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13736
dc.source2023 15th International Conference on Information Technology and Electrical Engineering Icitee 2023
dc.subjectDeep Neural Network
dc.subjectMyocardial Perfusion Imaging
dc.subjectQualitative Analysis
dc.subjectStenosis Classification
dc.subjectTransfer Learning
dc.titleDeep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images
dc.typeConference Paper

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