Super Resolusi Brain Tumor MRI Menggunakan Deep Learning Super Resolution Brain Tumor MRI Using Deep Learning

Arsy, Izzatul (2025) Super Resolusi Brain Tumor MRI Menggunakan Deep Learning Super Resolution Brain Tumor MRI Using Deep Learning. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Magnetic Resonance Imaging (MRI) images of the brain play a crucial role in medical diagnosis. However, low image quality can hinder optimal interpretation of anatomical details. This study aims to demonstrate whether the Super-Resolution Convolutional Neural Network (SRCNN) method is capable of producing super-resolution images that are close to the quality of the original image (ground truth), based on the evaluation metrics Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The dataset used consists of 7023 brain MRI images. The SRCNN model was trained using the Adam optimizer, the Mean Squared Error (MSE) loss function, and a combination of the number of epochs and learning rate. The best model produced a PSNR value of 43.6918 dB and an SSIM of 0.9894. These findings indicate that the SRCNN model is capable of producing images that visually and structurally resemble the ground truth. Furthermore, the model was integrated into a web application using Python Flask, which allows users to upload low-resolution MRI images and obtain downloadable super-resolution versions. The results of this study demonstrate the potential of SRCNN in supporting practical improvements in the quality of medical images.

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