Ağırlıklı CNN Topluluğu Tabanlı Kolorektal Kanser Tespiti

Yıl 2024, Cilt: 40 Sayı: 2, 270 - 287, 31.08.2024

Mustafa Yurdakul , Sakir Tasdemır

Öz

Kolorektal Kanser(KKR), dünya çapında yaygın ve potansiyel olarak ölümcül bir hastalıktır. Erken ve doğru teşhis, teşhisin zaman alması, insan hatalarının olasılığı ve uzman doktor eksikliği nedeniyle zor bir süreçtir. Bu çalışmada, tıbbi görüntülerden kolorektal kanserin teşhisini basitleştirmek ve hızlandırmak için derin öğrenme algoritmaları kullanılmıştır. Çeşitli KKR evrelerini içeren Enteroskop Biyopsi Histopatolojik H&E(EBHI) görüntü veri seti kullanıldı. Çeşitli önceden eğitilmiş Evrişimli Sinir Ağı modelleri, görüntüleri iyi huylu veya kötü huylu olarak sınıflandırmak için kullanıldı. Ayrıca, sınıflandırma doğruluğunu artırmak için üç en iyi model ağırlıklı topluluk yöntemiyle birleştirildi. Deneysel sonuçlar, ağırlıklı topluluk yönteminin sınıflandırma performansını önemli ölçüde iyileştirdiğini göstermektedir.

Anahtar Kelimeler

Ağırlıklı Topluluk Öğrenmesi, Derin Öğrenme, Kolorektal Kanser, Konvolüsyonel Sinir Ağı

Kaynakça

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