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المؤلفDegerli, Aysen
المؤلفSohrab, Fahad
المؤلفKiranyaz, Serkan
المؤلفGabbouj, Moncef
تاريخ الإتاحة2023-09-24T08:57:19Z
تاريخ النشر2022
اسم المنشورComputing in Cardiology
المصدرScopus
الرقم المعياري الدولي للكتاب2325-8861
معرّف المصادر الموحدhttp://dx.doi.org/10.22489/CinC.2022.242
معرّف المصادر الموحدhttp://hdl.handle.net/10576/47896
الملخصMyocardial infarction (MI) is the leading cause of mortaZity and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a frame-work for early detection of MI over multi-view echocardio-graphy that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.
راعي المشروعThis study was supported in part by the NSF-Business Finland Center for Visual and Decision Informatics (CVDI) Advanced Machine Learning for Industrial Applications (AMaLIA) under Grant 4183/31/2021, and in part by the Haltian Stroke-Data projects.
اللغةen
الناشرIEEE Computer Society
الموضوعImage and Video Processing (eess.IV)
Computer Vision and Pattern Recognition (cs.CV)
العنوانEarly Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
النوعConference Paper
الصفحات-
رقم المجلد2022-September
dc.accessType Abstract Only


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