Derin Öğrenme ile Beyin MRI Görüntülerinde Süper Çözünürlük: SRCNN, SRGAN ve ESRGAN Yaklaşımları
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Keywords:
Süper Çözünürlük, Beyin MRI Görüntüleri, SRCNN, SRGAN, ESRGAN, Derin Öğrenme, Tıbbi GörüntülemeAbstract
Tıbbi görüntüleme teknolojileri, hastalıkların doğru teşhis ve tedavisinde hayati bir öneme sahiptir.
Beyin MRI görüntüleri, nörolojik hastalıkların tanı ve takibinde detaylı anatomik bilgi sunar. Ancak,
teknik sınırlamalar ve donanım kapasiteleri nedeniyle elde edilen MRI görüntüleri çoğu zaman düşük
çözünürlüklü olmaktadır. Bu çalışmanın amacı, düşük çözünürlüklü beyin MRI görüntülerini iyileştirmek
için SRCNN (Super-Resolution Convolutional Neural Network), SRGAN (Super-Resolution Generative
Adversarial Network) ve ESRGAN (Enhanced Super-Resolution Generative Adversarial Network)
yöntemlerinin uygulanması ve karşılaştırılmasıdır. Modellerin performansları, görsel kalite, yapısal
benzerlik indeksi (SSIM) ve tepe sinyal-gürültü oranı (PSNR) gibi ölçütlerle değerlendirilmiştir. Elde
edilen bulgular, ESRGAN modelinin daha keskin detaylar ve daha gerçekçi görüntüler ürettiğini ortaya
koymaktadır. Bu çalışma, derin öğrenme tabanlı süper çözünürlük tekniklerinin tıbbi görüntülemede
sunduğu yenilikçi olanaklara dikkat çekmektedir.
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