Aktivasyon fonksiyonları ve derin öğrenme mimarilerinin beyin MRI görüntüleri üzerindeki sınıflandırma performanslarının incelenmesi

Yıl 2026, Cilt: 41 Sayı: 1 , 519 - 532 , 31.03.2026

Yasin Özkan

https://doi.org/10.17341/gazimmfd.1631531

https://izlik.org/JA34AJ64PG

Öz

Beyin tümörü tespiti, tıbbi görüntüleme alanında en kritik süreçlerden biri olup, manyetik rezonans görüntüleme (MRI) yüksek çözünürlüklü yumuşak doku detayları sayesinde tanı sürecinde önemli bir rol oynamaktadır. Ancak MRI görüntülerinin manuel olarak değerlendirilmesi zaman alıcı ve hataya açık bir süreçtir. Bu nedenle, derin öğrenme tabanlı otomatik sistemlerin geliştirilmesi, tanı hızını artırmak ve sağlık hizmetlerinin etkinliğini yükseltmek açısından büyük önem taşımaktadır. Bu çalışmada, Brain MRI görüntülerinden oluşan bir veri seti kullanılarak beyin tümörlerinin sınıflandırılması amaçlanmıştır. Öncelikle, veri setine görüntü işleme teknikleri uygulanarak iyileştirilmiş görüntüler elde edilmiştir. Ardından, orijinal ve iyileştirilmiş görüntüler yedi farklı transfer öğrenme mimarisine girdi olarak verilmiştir. Sonuçlar, tüm modellerde iyileştirilmiş görüntülerin daha yüksek doğruluk sağladığını göstermiştir. Çalışmanın ikinci aşamasında, en başarılı iki model olan ResNet101 ve ResNet152 üzerinde farklı aktivasyon fonksiyonları test edilmiştir. Önerilen hibrit aktivasyon fonksiyonu ile ResNet101 %98,42, ResNet152 ise %97,20 doğruluk oranına ulaşarak en yüksek performansı göstermiştir. Bu bulgular, önerilen yöntemin sınıflandırma başarısını önemli ölçüde artırdığını ortaya koymaktadır.

Anahtar Kelimeler

Beyin tümörü sınıflandırması , MRI görüntü , hibrit aktivasyon fonksiyonları , transfer öğrenme , tıbbi görüntü

Etik Beyan

Bu çalışmada kullanılan tüm veri setleri, halka açık ve ücretsiz erişime sahip olan veri kaynaklarından temin edilmiştir. Veri setleri, belirli etik kurallar ve gizlilik ilkelerine uygun olarak sağlanmış ve hiçbir kişisel bilgi içermemektedir. Çalışma, veri sahiplerinin onaylarını ve ilgili yasal düzenlemeleri göz önünde bulundurarak gerçekleştirilmiştir. Kullanılan veri setleri, yalnızca akademik amaçlarla ve araştırma bağlamında kullanılmıştır. Bu araştırmada herhangi bir etik ihlal bulunmamaktadır.

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