عرض بسيط للتسجيلة

المؤلفDegerli A.
المؤلفZabihi M.
المؤلفKiranyaz, Mustafa Serkan
المؤلفHamid T.
المؤلفMazhar R.
المؤلفHamila R.
المؤلفGabbouj M.
تاريخ الإتاحة2022-04-26T12:31:19Z
تاريخ النشر2021
اسم المنشورIEEE Access
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ACCESS.2021.3059595
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100930652&doi=10.1109%2fACCESS.2021.3059595&partnerID=40&md5=8158be062bf0b8189d43d92af9bd9299
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30601
الملخصMyocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further heart tissue damages or death. The earliest and most reliable sign of ischemia is regional wall motion abnormality (RWMA) of the affected part of the ventricular muscle. Echocardiography can easily, inexpensively, and non-invasively exhibit the RWMA. In this article, we introduce a three-phase approach for early MI detection in low-quality echocardiography: 1) segmentation of the entire left ventricle (LV) wall using a state-of-the-art deep learning model, 2) analysis of the segmented LV wall by feature engineering, and 3) early MI detection. The main contributions of this study are highly accurate segmentation of the LV wall from low-quality echocardiography, pseudo labeling approach for ground-truth formation of the unannotated LV wall, and the first public echocardiographic dataset (HMC-QU)a MI detection. Furthermore, the outputs of the proposed approach can significantly help cardiologists for a better assessment of the LV wall characteristics. The proposed approach has achieved 95.72% sensitivity and 99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03% specificity, and 86.85% precision for MI detection on the HMC-QU dataset.aThe benchmark HMC-QU dataset is publicly shared at the repository https://www.kaggle.com/aysendegerli/hmcqu-dataset
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعArtificial life
Cardiology
Deep learning
Diagnosis
Early diagnosis
Feature engineerings
Highly accurate
Learning models
Left ventricles
Low qualities
Myocardial Infarction
State of the art
Echocardiography
العنوانEarly Detection of Myocardial Infarction in Low-Quality Echocardiography
النوعArticle
الصفحات34442-34453
رقم المجلد9
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة