Benzerlik Tabanlı Öğrenme Kullanarak Histopatolojik Görüntülerin Sınıflandırılması

Year 2024, Volume: 14 Issue: 2, 49 - 58, 30.07.2024

Mahmut Kaya Hasan Şakir Bilge

Abstract

Benzerlik metriği öğrenme, bir uzaklık öğrenme yaklaşımı olup aynı sınıfa ait örnekler arasındaki benzerliği arttırmayı, farklı sınıflar arasındaki benzerliği ise azaltmayı hedeflemektedir. Son yıllarda derin öğrenmenin elde ettiği başarıyla beraber benzerlik metriği öğrenmenin derin ağ modellerinde başarıyla uygulanabileceği görülmüştür. Bu çalışmada derin metrik öğrenme modellerinden olan Siamese ve Triplet ağ modelleri kullanılarak histopatolojik görüntülerde bir sınıflandırma işlemi gerçekleştirilmiştir. Histopatolojik görüntüler radyologlar tarafından tanı amacıyla kullanılırken sağlıklı ve sağlıksız görüntülerini birbirinden ayırmak oldukça zorlayıcıdır. Bu çalışmada literatürde başarısını kanıtlamış olan transfer öğrenme yöntemleri derin metrik öğrenme modellerine entegre edilmiştir. Yapılan çalışma sonucunda Siamese+VGG19 ağ modelinde %95,39 başarı elde edilirken, Triplet+VGG19 ağ modelinde ise %96,92 sınıflandırma başarısı elde edilmiştir.

Keywords

Benzerlik metriği öğrenme, siamese, triplet, sınıflandırma

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