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PENINGKATAN KUALITAS CITRA JANTUNG JANIN MENGGUNAKAN PENDEKATAN SUPER RESOLUTION

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Penilaian

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The image of the fetal heart is the image produced by ultrasound, where the image is used in the health sector to obtain information about the development of the fetus in the womb. The image produced by ultrasound does not fully obtain detailed and detailed information. However, in the medical field, two-dimensional ultrasound, in general, is still widely applied to obstetricians because it is still considered appropriate in obstetrical examinations. This becomes one of the challenges, especially in the poor quality of the fetal heart image, both in the protocol and the different variations in each patient. Therefore, to overcome these problems, it is necessary to increase the resolution and accuracy of single image super-resolution which is faster on deep learning. In this research, image quality improvement uses a deep learning method with a super-resolution approach. The deep learning methods used are super-resolution residual network, super�resolution generative adversarial network, and super-resolution convolutional neural network. Image quality improvement is carried out by using low resolution and high-resolution images or enhancements from the initial video data, with a total of 144 models designed. The data will be trained and tested using fetal heart image data which are few in data access. Each model is designed with a combination of parameters such as epoch, batch size, learning rate, optimizer, and upscale. From the results of testing 144 models that have been designed, the model that produces the best performance is model 9 using the super-resolution convolutional neural network method, the model uses parameters epoch 1000, batch size 64, learning rate 0.0001, and Adam optimizer. This model produces the best evaluation with MSE 11.24343, SSIM 0.96637/96.637, and PSNR 34.76977 dB, while the unseen data results obtained are MSE 11.28212, SSIM 0.96082/96.082, and PSNR 34.96804 dB.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002044	T73694	T736942022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T736942022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xix, 99 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.360 7

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)

Program aplikasi
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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