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Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification
المؤلفون المشاركون
Cui, Lizhen
Xiao, Xiaoyan
Yang, Meijun
Liu, Zhi
Sun, Longkun
Sun, Dianmin
Zhang, Pengfei
Guo, Wei
Yang, Guang
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-01
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
الملخص EN
Background.
Currently, echocardiography has become an essential technology for the diagnosis of cardiovascular diseases.
Accurate classification of apical two-chamber (A2C), apical three-chamber (A3C), and apical four-chamber (A4C) views and the precise detection of the left ventricle can significantly reduce the workload of clinicians and improve the reproducibility of left ventricle segmentation.
In addition, left ventricle detection is significant for the three-dimensional reconstruction of the heart chambers.
Method.
RetinaNet is a one-stage object detection algorithm that can achieve high accuracy and efficiency at the same time.
RetinaNet is mainly composed of the residual network (ResNet), the feature pyramid network (FPN), and two fully convolutional networks (FCNs); one FCN is for the classification task, and the other is for the border regression task.
Results.
In this paper, we use the classification subnetwork to classify A2C, A3C, and A4C images and use the regression subnetworks to detect the left ventricle simultaneously.
We display not only the position of the left ventricle on the test image but also the view category on the image, which will facilitate the diagnosis.
We used the mean intersection-over-union (mIOU) as an index to measure the performance of left ventricle detection and the accuracy as an index to measure the effect of the classification of the three different views.
Our study shows that both classification and detection effects are noteworthy.
The classification accuracy rates of A2C, A3C, and A4C are 1.000, 0.935, and 0.989, respectively.
The mIOU values of A2C, A3C, and A4C are 0.858, 0.794, and 0.838, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yang, Meijun& Xiao, Xiaoyan& Liu, Zhi& Sun, Longkun& Guo, Wei& Cui, Lizhen…[et al.]. 2020. Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Scientific Programming،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1209098
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yang, Meijun…[et al.]. Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Scientific Programming No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1209098
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yang, Meijun& Xiao, Xiaoyan& Liu, Zhi& Sun, Longkun& Guo, Wei& Cui, Lizhen…[et al.]. Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1209098
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1209098
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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