Super Resolusi Chest X-Ray Images Pneumonia Menggunakan Generative Adversarial Networks (Gans) Super-Resolution of Pneumonia Chest X-Ray Images Using Generative Adversarial Networks (GANs)

Oktaviana, Wulan (2025) Super Resolusi Chest X-Ray Images Pneumonia Menggunakan Generative Adversarial Networks (Gans) Super-Resolution of Pneumonia Chest X-Ray Images Using Generative Adversarial Networks (GANs). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Low-resolution medical images often hinder the diagnostic process due to the loss of critical details. This study aims to enhance the quality of low-resolution lung X-ray images using the Super-Resolution Generative Adversarial Network (SRGAN) method. The SRGAN architecture comprises two primary networks—the Generator and the Discriminator—trained adversarially. The training process employs a combination of L1, L2, Binary Cross-Entropy (BCE), and adversarial loss functions to produce sharper, more detailed high-resolution (SR) images. The model was evaluated on an X-ray dataset containing two categories: normal lungs and lungs with pneumonia. Evaluations using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics demonstrated significant improvements in image quality, achieving peak values ​​of 32.71 dB for PSNR and 0.8890 for SSIM. In addition to numerical evaluation, the SR results were visualized and compared against ground-truth images to assess visual structural quality. The optimal model was subsequently implemented in a Flask-based web application, enabling users to upload low-resolution (LR) images and automatically obtain SR outputs. The findings indicate that SRGAN holds significant potential for enhancing medical image quality in clinical practice.

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