KLASIFIKASI SUDUT PANDANG JANTUNG JANIN DARI MEDIA ULTRASONOGRAPHY MEGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

IRAWAN, IRAWAN and Nurmaini, Siti (2021) KLASIFIKASI SUDUT PANDANG JANTUNG JANIN DARI MEDIA ULTRASONOGRAPHY MEGGUNAKAN CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011281621057_0002085908.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011281621057_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (8MB) | Request a copy
Preview	Text RAMA_56201_09011281621057_0002085908_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB) | Preview
	Text RAMA_56201_09011281621057_0002085908_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011281621057_0002085908_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011281621057_0002085908_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011281621057_0002085908_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (502kB) | Request a copy
	Text RAMA_56201_09011281621057_0002085908_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (482kB) | Request a copy
	Text RAMA_56201_09011281621057_0002085908_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy

Abstract

Heart is a vital organ in human body, however congengital heart disease is an anomaly in fetal body structure that frequently happens, because of that fetal echocardiograph is done by pediatric cardiologist to identify the congengital heart disease on fetal. Convolutional Neural Network (CNN) is the method used by the authors in this study. Here will be classified 4 screening views of fetal heart using CNN with the pretrain model architecture VGG16, VGG19 and ResNet-50, where the main focus of the research is the average f1-score of the models produced during training and testing. CNN's classification yielded average f1-score of 100% for VGG16, and 99% for VGG19 and ResNet-50 for model training. After the trained model are obtained, it is used to classify the dataset outside the training data, the result is that the average f1-score is 99% for VGG16, 87% for VGG19, and 69% for ResNet50. VGG16 has the best results for classifying the used dataset.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Fetal Heart, VGGNET, ResNet, Screening view, Ultrasonography, Convolutional Neural Network.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 10109 not found.
Date Deposited:	20 Jan 2021 06:29
Last Modified:	20 Jan 2021 06:29
URI:	http://repository.unsri.ac.id/id/eprint/40669

Actions (login required)

View Item