Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım

Kolon kanseri, gelişmiş ülkelerde ciddi bir sağlık sorunu olmakta ve en sık görülen kanser türleri arasında gelmektedir. Bu hastalığın erken teşhisi hastaların hayatta kalma şansını artırmaktadır. Geciken teşhisler ise ölümle sonuçlanabilmektedir. Bu çalışmada kolon kanseri tespiti için EfficientNetB0 ve destek vektör makineleri (DVM) tabanlı bir model önerilmiştir. EfficientNetB0 mimarisi ile histopatolojik görüntülerden öznitelik haritalarının çıkarılması sağlanırken, DVM algoritması ile elde edilen öznitelik haritalarının sınıflandırılması gerçekleştirilmektedir. Ayrıca önerilen modelin başarısını analiz etmek üzere EfficientNetB0, Xception, VGG19, InceptionV3, DenseNet121 ve ResNet101 gibi evrişimli sinir ağları (ESA) mimarileri ile performans kıyaslaması yapılmıştır. Veri kümesi olarak sekiz sınıflı Kather-5k ve iki sınıflı LC25000 veri kümeleri kullanılmıştır. Elde edilen bulgular, önerilen modelin Kather-5k veri kümesi kullanıldığında %99.70 doğruluk, %100 kesinlik, %100 duyarlılık, %100 F1-Score, %99.71 G-ortalama, %100 özgüllük ve %99.83 AUC ile mevcut ESA mimarilerine kıyasla daha yüksek başarı sağladığını göstermiştir. LC25000 veri kümesi kullanıldığında ise önerilen model tüm metriklerde %100 başarı elde etmiştir. Benzer şekilde Kather-5k ve LC25000 veri kümeleri birleşiminden oluşan veri kümesi kullanıldığında önerilen model, %99.96 doğruluk, %100 kesinlik, %100 duyarlılık, %100 F1-Score, %99.92 G-ortalama, %100 özgüllük ve %99.96 AUC oranı ile diğer modellere kıyasla daha yüksek performans göstermiştir. Ayrıca önerilen model ile EfficientNetB0 mimarisinin başarısında önemli oranda bir başarı artışı sağlanmıştır.

Anahtar Kelimeler:

Kolon kanseri, evrişimli sinir ağları, EfficientNetB0, destek vektör makineleri

EfficientNetB0 and SVM Based Approach for Colon Cancer Recognition from Histopathological Images

Colon cancer is a significant health issue in developed countries and ranks among the most common types of cancer. Early diagnosis of this disease increases the chances of survival for patients, while delayed diagnosis can lead to fatal outcomes. In this study, an EfficientNetB0 and Support Vector Machines (SVM) based model has been proposed for colon cancer detection. The EfficientNetB0 architecture is utilized to extract feature maps from histopathological images, and the SVM algorithm is employed to classify the obtained feature maps. Furthermore, to analyze the performance of the proposed model, a comparison is made with convolutional neural network (CNN) architectures such as EfficientNetB0, Xception, VGG19, InceptionV3, DenseNet121, and ResNet101. The datasets used for the study are the eight-class Kather-5k and the two-class LC25000 datasets. The findings indicate that the proposed model achieves higher success rates compared to existing CNN architectures on the Kather-5k dataset, with an accuracy of 99.70%, precision of 100%, recall of 100%, F1-Score of 100%, G-mean of 99.71%, specificity of 100%, and an AUC of 99.83%. Similarly, on the LC25000 dataset, the proposed model achieves 100% success rates in all metrics. When the combined dataset of Kather-5k and LC25000 is used, the proposed model demonstrates better performance compared to other models with an accuracy of 99.96%, precision of 100%, recall of 100%, F1-Score of 100%, G-mean of 99.92%, specificity of 100%, and an AUC of 99.96%. In addition, with the proposed model, a significant increase in success has been achieved in the success of the EfficientNetB0 architecture.

Keywords:

Colon cancer, convolutional neural networks, EfficientNetB0, support vector machines,

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