Detection of colon cancer using k-means and deep learning algorithms on histopathological images

Yıl 2025, Cilt: 31 Sayı: 5, 821 - 832, 19.10.2025

Ulaş Yurtsever , Hayrettin Evirgen , Mustafa Avunduk

Öz

In this research, a novel approach for classifying colon cancer was developed by employing two convolutional neural network (CNN) models, namely GoogLeNet and AlexNet. This approach involves training CNNs with histopathological images segmented into color clusters using an augmented k-means clustering algorithm, rather than utilizing original-raw images. This method was applied to 20 datasets with distinct structural and characteristic features, derived from larger datasets comprising both original and segmented images. The datasets were used to train and test CNN models. The results indicate that AlexNet, trained with segmented images, showed a 2% to 23% increase in accuracy performance, while GoogLeNet's accuracy performance improved by 2% to 27%. Notably, the proposed approach yielded higher accuracy with datasets containing non-homogeneous data.

Anahtar Kelimeler

Convolutional neural network , Deep learning , Image segmentation , Image classification , Colon cancer

Histopatolojik görüntüler üzerinde k-ortalamalar ve derin öğrenme algoritmaları kullanılarak kolon kanseri tespiti

Öz

Bu araştırmada, GoogLeNet ve AlexNet olmak üzere iki evrişimli sinir ağı (CNN) modeli kullanılarak kolon kanserinin sınıflandırılması için yeni bir yaklaşım geliştirilmiştir. Bu yaklaşımda CNN'ler, orijinal ham görüntüleri kullanmak yerine, artırılmış bir k-ortalamalar kümeleme algoritması kullanılarak renk kümelerine ayrılmış histopatolojik görüntüleri kullanarak eğitilmektedir. Bu yöntem hem orijinal hem de bölütlenmiş görüntülerden oluşan daha büyük veri kümelerinden elde edilen farklı yapısal ve karakteristik özelliklere sahip 20 veri kümesine uygulanmıştır. Veri kümeleri CNN modellerini eğitmek ve test etmek için kullanılmıştır. Sonuçlar, bölümlere ayrılmış görüntülerle eğitilen AlexNet'in doğruluk performansında %2 ile %23 arasında bir artış gösterdiğini, GoogLeNet'in doğruluk performansının ise %2 ile %27 arasında iyileştiğini ortaya koymuştur. Özellikle, önerilen yaklaşım homojen olmayan verilere sahip veri kümelerinde daha yüksek doğruluk sağlamıştır.

Anahtar Kelimeler

Evrişimli sinir ağı , Derin öğrenme , Görüntü bölütleme , Görüntü sınıflandırma , Kolon kanseri

Kaynakça

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