Meme Kanseri Histopatalojik Görüntülerinin Konvolüsyonal Sinir Ağları ile Sınıflandırılması

Öz

Meme kanseri, dünya çapında kadınlar arasında en fazla ölümün görüldüğü kanser türüdür. Meme kanseri imgelerinin bilgisayar destekli sistemler yardımıyla hızlı ve doğru bir şekilde sınıflandırılması hayati önem arz etmektedir. Bu çalışmada, meme kanseri imgelerini iyi ve kötü huylu olarak sınıflandırmak için ResNet-50 mimarisi önerilmiştir. Evrişimsel Sinir Ağı tabanlı ResNet-50 mimarisi kullanılarak, açık kaynak BreakHis veri setindeki, meme kanseri imgelerinin ikili sınıflandırılması gerçekleştirilmiştir. ResNet-50 mimarisinin eğitiminde transfer öğrenme yöntemi uygulanmıştır. Önerilen modelin sınıflandırma başarısının, literatürdeki mevcut çalışmalara kıyasla daha yüksek olduğu gözlemlenmiştir. Ayrıca önerilen model, meme kanseri imgeleri üzerinde herhangi bir ön işleme yapmadan verileri otomatik olarak sınıflandırmaktadır.

Anahtar Kelimeler

• Meme kanseri,Tıbbi görüntü analizi,Derin öğrenme,KSA

Classification of Histopathological Breast Cancer Images using Convolutional Neural Networks

Öz

Breast cancer is the most common form of cancer that causes death among women worldwide. The automated classification of breast cancer images with the help of computer aided systems is significantly important for early intervention. In this study, transfer learning method was used to classify breast cancer biopsy images as benign and malignant. Binary classification of breast cancer images from open source BreakHis data set was performed using the ResNet-50 convolutional neural network (CNN) architecture. The classification accuracy of the proposed model is higher than all existing studies on the BreakHis dataset. In addition, the proposed model provides automatic classification of breast cancer images without manual feature extraction or any pre-processing on images.

Anahtar Kelimeler

• Breast cancer,Medical image analysis,Deep learning,CNN

Kaynakça

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