Cilt Lezyonlarının Sınıflandırılmasında Derin Öğrenme Tabanlı Bir Yöntem

Year 2021, , 30 - 36, 25.06.2021

Volkan Göreke

https://doi.org/10.46810/tdfd.797683

Cited By: 2

Abstract

Cilt kanseri kötü huylu tümörlerin kontrolsüz çoğalması ile başlar. Dünya çapında sık karşılaşılan bir kanser türüdür. Uzman hekimler tarafından çıplak gözle incelemesi ve teşhis konulması güçtür. Bu yüzden bilgisayar destekli teşhis sistemleri hekimlere tanı koymada yardımcı olabilir. Bu sistemler günümüzde yapay zekanın bir türü olan derin sinir ağlarını yaygın olarak kullanır. Pek çok derin sinir ağı içeren çalışmada veri girişi olarak medikal görüntüler kullanılır. Ağ mimarisine bağlı olarak bu sistemler öznitelikleri kendi katmanlarında çıkarırlar. Bu çalışmada VGG16 ön eğitimli derin sinir ağı kullanılarak ilk önce ağ katmanlarından görüntülere ilişkin öznitelikler elde edilmiştir. Daha sonra yüksek miktarda veri içeren bu özniteliklerin boyutu azaltılmıştır. Böylece sınıflandırmada en iyi başarımı sağlayacak öznitelikler elde edilmiştir. Veri artırma algoritması kullanılarak elde edilen nümerik veri artırılmış ve CNN tür derin sinir ağında %96 sınıflandırma doğruluğu ve %100 AUC başarımı elde edilmiştir.

Keywords

Cilt kanseri, derin sinir ağları, veri artırma

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