Skripsi

PERBANDINGAN KINERJA SEGMENTASI JANTUNG JANIN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Congenital heart disease is one of the leading causes of death in the first year of birth. One example of the challenges that exist in medical images, especially the fetal heart, is the poor image quality. In fetal heart echocardiography, the problem that occurs when diagnosing congenital heart disease is that the ultrasound image obtained is susceptible to blurry parts that can damage the image and reduce image quality. Segmentation of the fetal heart using deep learning can help doctors to diagnose congenital heart disease more quickly. The method used in this research is Convolutional Neural Network (CNN) with FractalNet, Resnet and U-Net architectures. In this study, the scenario carried out is to segment 7 classes with the number of models for each class that is opened 12 for the learning rate parameters, and the best loss function. Of the 12 models tested in each class. segmentation of the fetal heart in classes la, lv, ra, rv, hole, aorta, and fetal heart got the results of the dice coefficient 94.23%, 97.44%, 97.83%, 97.37%, 92.17%, 94 0.04%, 90.85%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002420	T49523	T495232021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T495232021

Publisher

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

Collation

xviii, 163 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.607

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Komputer
Komputer Grafis, Grafika Komputer, digital image

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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