gummfd Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1300-1884 1304-4915 Gazi Üniversitesi 10.17341/gazimmfd.1631531 Deep Learning Neural Networks Derin Öğrenme Nöral Ağlar Aktivasyon fonksiyonları ve derin öğrenme mimarilerinin beyin MRI görüntüleri üzerindeki sınıflandırma performanslarının incelenmesi https://orcid.org/0000-0002-2029-0856 Özkan Yasin ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ 03 31 2026 41 1 519 532 02 02 2025 01 16 2026 Copyright © 1986, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1986 Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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.

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