Breast Cancer Histopathological Image Classification

Year 2021, , 87 - 94, 30.01.2021

Ebru Erdem , Tolga Aydin

https://doi.org/10.17671/gazibtd.746673

Cited By: 1

Abstract

Breast cancer is one of the most common types of cancer in women worldwide, after lung cancer. Early diagnosis and classification of cancer can positively affect the healing process of patients. In this study, deep learning approaches for cancer detection in chest histopathological images are presented. The success of deep learning architectures may vary depending on the problem. In this study, classification is made using pre-trained CNN architectures, VGG16, Inception-V3, and the network (VGG16 + Inception-V3), which is a combination of two deep neural networks. The concatenate network structure in the study was named as VIHist. The performance of the proposed approaches has been examined on the BreakHist dataset. The images used for detection are 40X magnified image slides. In results, the concatenate network structure (VIHist) gave the highest accuracy rate with 99.03% accuracy. Inception-V3 network showed ~ 6% superior performance than VGG16 deep neural network. Although there is no pathology knowledge on the disease, 98.3% ± 1% accuracy was achieved in the detection of diseases with the proposed deep learning architectures. When the results are examined, it is seen that the performance is higher than the successful studies in the literature.

Keywords

breast cancer, Inception-V3, VGG16

Göğüs Kanseri Histopatolojik Görüntü Sınıflandırması

Abstract

Meme kanseri, dünya genelinde kadınlarda, akciğer kanserinden sonra en çok rastlanan kanser türlerinden biridir. Kanserin erken teşhisi ve sınıflandırılması hastaların iyileşme sürecine olumlu etki edebilmektedir. Bu çalışmada, göğüs histopatolojik görüntülerinde kanser tespiti için derin öğrenme yaklaşımları sunulmuştur. Derin öğrenme mimarilerinin başarısı probleme özgü değişebilmektedir. Bu çalışmada, önceden eğitilmiş CNN mimarilerinden, VGG16, Inception-V3 ve iki derin sinir ağın birleşimi olan ağ (VGG16+Inception-V3) kullanılarak sınıflandırma yapılmıştır. Çalışma içerisinde birleştirme ağ yapısı VIHist olarak adlandırılmıştır. Önerilen yaklaşımların performansı, BreakHist veri seti üzerinde incelenmiştir. Tespit için kullanılan görüntüler 40X büyütülmüş görüntü slaytlarıdır. Elde edilen bulgularda, %99.03 başarı ile birleştirme ağ yapısı (VIHist) en yüksek doğruluk oranını vermiştir. Inception-V3 ağı, VGG16 derin sinir ağına göre ~%6 daha üstün performans göstermiştir. Hastalık üzerinde patoloji bilgisine sahip olunmamasına rağmen, önerilen derin öğrenme mimarileri ile hastalık tespitinde %98.3 ± %1 başarı elde edilmiştir. Sonuçlar incelendiğinde, literatürdeki başarılı çalışmalara göre performansın daha yüksek bulunduğu görülmüştür.

Keywords

Göğüs kanseri, Inception-V3, VGG16

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