Derin Öğrenme Algoritmalarıyla Meme Kanserinin Histopatolojik Görüntü Sınıflandırması

Abstract

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.

Keywords

• Derin öğrenme
• Meme kanseri
• Histopolojik görüntü

Histopathologic Image Classification of Breast Cancer Using Deep Learning Algorithms

Abstract

This research aims to compare the performance of different deep learning models used in breast cancer diagnosis to determine the most successful model. Breast cancer is one of the most common and deadly cancers worldwide, and early detection of breast cancer significantly increases the survival rate of patients. In recent years, deep learning-based models have made great progress in the field of medical image analysis and have started to play an important role in the diagnosis process, especially by achieving high accuracy rates on histopathological images. In this research, EfficientNet-Swin, VGG16, ResNet50 and Hybrid CNN-LSTM-Quantum models are compared and their classification performances are evaluated using metrics such as Precision, Recall, F1-Score, Accuracy and AUC. The results show that the EfficientNet-Swin model achieves the highest success rate with 92% accuracy and 97% ROC-AUC. As a transformer-based model, EfficientNet-Swin has better generalization capacity and strong feature extraction capability compared to traditional CNN models. The Hybrid CNN-LSTM-Quantum model offers an innovative approach by combining deep learning and quantum computing techniques. This model has emerged as a promising method, especially in biomedical imaging applications that require time series analysis. The VGG16 model is notable for its low false positive rate, while the ResNet50 model requires additional optimization due to the risk of overlearning. The findings of the study show that transformer-based models have higher accuracy and generalization capacity compared to traditional CNN architectures. In particular, the EfficientNet-Swin model was found to be the most suitable model for clinical use for breast cancer diagnosis. Future work should focus on testing these models on larger and more diverse datasets to ensure their clinical integration. Furthermore, the development of quantum computing-enabled hybrid models can further improve the accuracy and efficiency of deep learning-based diagnostic systems.

Keywords

• Deep learning
• Breast cancer
• Histopologic image

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