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PERBAIKAN KUALITAS CITRA CHEST X-RAY DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Chest x-ray image are an important tool in the diagnosis of lung and chest diseases. However, these images are often exposed to noise that can reduce their quality and usefulness. In this study, researchers performed chest x-ray image repair that has gaussian noise using Convolutional Neural Network (CNN) and compared the application of CNN methods from both CNN models, namely DnCNN and IRCNN in improving the quality of chest x-ray images that are disturbed by noise or interference from outside factors. The results of this study show that the DnCNN model has a greater average PSNR value than the IRCNN model with an average PSNR value 30.95 dB and SSIM 0.85. Meanwhile, the IRCNN model has an SSIM value that is greater than the DnCNN model with an average PSNR value 30.6 dB and SSIM 0.86.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002980	T120963	T1209632023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1209632023

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xvi, 61 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.754 07

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)

Jurusan Teknik Informatika
Situs Jejaring Sosial

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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