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

ARFANDI, MUHAMMAD IRVAN and Supardi, Julian and Rizqie, M. Qurhanul (2023) PERBAIKAN KUALITAS CITRA CHEST X-RAY DENGAN METODE CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Citra chest x-ray, CNN, Convolutional Neural Network, DnCNN, IRCNN.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Irvan Arfandi
Date Deposited:	24 Jul 2023 08:26
Last Modified:	24 Jul 2023 08:26
URI:	http://repository.unsri.ac.id/id/eprint/120963

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