TY  - JOUR
T1  - Derin Öğrenme Algoritmalarıyla Meme Kanserinin Histopatolojik Görüntü Sınıflandırması
TT  - Histopathologic Image Classification of Breast Cancer Using Deep Learning Algorithms
AU  - Ertürkmen, Ejder
AU  - Öter, Ali
PY  - 2025
DA  - November
Y2  - 2025
DO  - 10.34248/bsengineering.1644212
JF  - Black Sea Journal of Engineering and Science
JO  - BSJ Eng. Sci.
PB  - Karyay Karadeniz Yayımcılık Ve Organizasyon Ticaret Limited Şirketi
WT  - DergiPark
SN  - 2619-8991
SP  - 1697
EP  - 1714
VL  - 8
IS  - 6
LA  - tr
AB  - Bu çalışma, meme kanseri teşhisinde kullanılan farklı DL modellerinin performanslarını karşılaştırarak en başarılı modelin belirlenmesini amaçlamaktadır. Meme kanseri, dünya genelinde en yaygın ve ölümcül kanser türlerinden biri olup, erken teşhis edilmesi hastaların sağkalım oranlarını önemli ölçüde artırmaktadır. Son yıllarda DL tabanlı modeller, tıbbi görüntü analizi alanında büyük ilerlemeler kaydetmiş ve özellikle histopatolojik görüntüler üzerinde yüksek doğruluk oranları elde edilerek teşhis sürecinde önemli bir rol oynamaya başlamıştır. Çalışmada, EfficientNet-Swin, VGG16, ResNet50 ve Hibrit CNN-LSTM-Quantum modelleri karşılaştırılmış ve bu modellerin sınıflandırma performansları kesinlik (Precision), duyarlılık (Recall), F1-Skoru, doğruluk (Accuracy) ve AUC gibi ölçütler kullanılarak değerlendirilmiştir. Elde edilen sonuçlar, EfficientNet-Swin modelinin %92 doğruluk ve %97 AUC değeri ile en yüksek başarı oranına ulaştığını göstermektedir. Transformer tabanlı bir model olan EfficientNet-Swin, geleneksel CNN modellerine kıyasla daha iyi genelleme kapasitesine ve güçlü öznitelik çıkarma yeteneğine sahiptir. Hibrit CNN-LSTM-Quantum modeli, DL ve kuantum hesaplama tekniklerini birleştirerek yenilikçi bir yaklaşım sunmaktadır. Bu model, özellikle zaman serisi analizi gerektiren biyomedikal görüntüleme uygulamalarında umut vadeden bir yöntem olarak öne çıkmıştır. VGG16 modeli, düşük yanlış pozitif oranı ile dikkat çekerken, ResNet50 modeli aşırı öğrenme riski nedeniyle ek optimizasyon gerektirmektedir. Çalışmadan elde edilen bulgular, transformer tabanlı modellerin geleneksel CNN mimarilerine kıyasla daha yüksek doğruluk ve genelleme kapasitesine sahip olduğunu göstermektedir. Özellikle EfficientNet-Swin modelinin, meme kanseri teşhisi için klinik kullanıma en uygun model olduğu belirlenmiştir. Gelecekteki çalışmalar, bu modellerin daha büyük ve çeşitli veri setleri üzerinde test edilerek klinik entegrasyonlarının sağlanmasına odaklanmalıdır. Ayrıca, kuantum hesaplama destekli hibrit modellerin geliştirilmesi, DL tabanlı teşhis sistemlerinin doğruluk ve verimliliğini daha da artırabilir.
KW  - Derin öğrenme
KW  - Meme kanseri
KW  - Histopolojik görüntü
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UR  - https://doi.org/10.34248/bsengineering.1644212
L1  - https://dergipark.org.tr/en/download/article-file/4629058
ER  -