Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study

<p dir="ltr">Congenital heart defects (CHDs) are a leading cause of death in infants under 1 year of age. Prenatal intervention can reduce the risk of postnatal serious CHD patients, but current diagnosis is based on qualitative criteria, which can lead to variability in diagnosis be...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Tawsifur Rahman (14150523) (author)
مؤلفون آخرون:	Mahmoud Khatib A. A. Al-Ruweidi (12535527) (author), Md. Shaheenur Islam Sumon (17983810) (author), Reema Yousef Kamal (17983813) (author), Muhammad E. H. Chowdhury (14150526) (author), Huseyin C. Yalcin (6695099) (author)
منشور في:	2023
الموضوعات:	Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Heart Echocardiography Videos Diseases Ultrasonic imaging Deep learning Convolutional neural networks Cardiovascular diseases Fetal heart rate Machine learning Congenital heart defects (CHDs) fetal echocardiogram CNN-LSTM stacking machine learning hypoplastic left heart syndrome (HLHS)
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study

مواد مشابهة