Skripsi

DETEKSI CACAT SEPTUM PADA JANTUNG JANIN SECARA REAL-TIME MENGGUNAKAN ARSITEKTUR MASK-RCNN

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Penilaian

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Congenital heart disease (CHD) or often referred to as congenital heart disease is a disease that causes the most common morbidity of death in developing and developed countries. Congenital heart defects have a mortality prevalence of between 5 and 9 in 1000 births, heart defects often occur in the first year of birth. In the medical world, the state of the heart can be detected through Ultrasonography (USG). The position of the hole in the heart in this study focused on the hole in each class, the atrial septal defect (ASD), the hole that is between the right and left atria. Furthermore, the ventricular septal defect (VSD) is the hole between the right and left ventricles, and the hole between the ASD and VSD is an atrioventricular septal defect (AVSD). The method used is object detection and segmentation of the Mask-RCNN architecture with a convolutional Neural network (CNN). The backbone used in this research is Resnet50 and Resnet101. The best model is obtained by adjusting the learning rate, epoch and batch size which has been increased, from the best model is obtained by using the resnet50 backbone with a Map value of 47%, IoU of 65%, Dsc of 71% and FPS of 10

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002555	T46710	T467102021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T467102021

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xv, 53 hlm,: ilus,; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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