Breast Cancer Detection on Histopathological Images Using Deep Neural Networks

Abstract

In this study, a model is proposed using deep neural network on histopathological images for the detection of breast cancer, which is one of the most dangerous diseases of our age and is very difficult to treat in case of late diagnosis. Within the study, computer-aided diagnosis systems are used as an assistant diagnostic method in the detection of breast cancer by reaching high accuracy values. The dataset has magnification ratios of 40X, 100X, 200X and 400X and contains a total of 7909 histopathological images. In the proposed deep neural network model, more successful results are obtained using four different pre-trained networks such as DenseNet201, Inception V3, ResNet50 and Xception. In order to further increase the performance of the models, dropout and data augmentation techniques are used. These networks are compared among themselves and the results obtained with the Xception network are performed to be more successful than other networks. In experimental studies carried out with the Xception network at the ratio of 200X magnification the most successful results are achieved with an accuracy score of 98.01, a precision value of 97.89 and recall value of 97.47 when comparing to other networks and other magnification ratios. The area value of under the ROC curve for the Xception network at 200X zoom ratio is 0.975. 

Keywords

• Breast cancer
• Histopathological image
• Classification
• Deep neural network
• pre-trained networks
• Xception

Derin Sinir Ağları Kullanarak Histopatolojik Görüntülerde Meme Kanseri Tespiti

Abstract

Bu çalışmada çağımızın en tehlikeli hastalıklarından birisi olan ve geç teşhis durumunda tedavisi oldukça zor bir hal alan meme kanserinin histopatolojik görüntülerde tespiti için derin sinir ağları kullanılarak bir model önerilmiştir. Çalışma ile bilgisayar destekli teşhis sistemlerinin yüksek doğruluk değerlerine ulaşarak meme kanserinin tespitinde yardımcı bir teşhis metodu kullanılması sağlanmıştır. Kullanılan veriseti 40X, 100X, 200X ve 400X yakınlaştırma oranlarına sahip ve toplamda 7909 adet histopatolojik görüntü içermektedir. Önerilen derin sinir ağı modelinde DenseNet201, Inception V3, ResNet50 ve Xception olmak üzere dört farklı ön-eğitimli (pre-trained) ağ kullanılarak daha başarılı sonuçlar elde edilmiştir. Modellerin başarımlarını daha da artırmak amacıyla bırakma (dropout) ve veri artırma (data augmentation) yöntemleri kullanılmıştır. Kullanılan bu ağlar kendi arasında karşılaştırılmış ve Xception ağı ile elde edilen sonuçların diğer ağlara oranla daha başarılı olduğu görülmüştür. Xception ağı ile 200X yakınlaştırma oranında gerçekleştirilen deneysel çalışmalarda, hem diğer ağlara göre hem de diğer yakınlaştırma oranlarına göre en başarılı sonuçlara ulaşılmış ve 98.01’lik bir doğruluk (Accuracy) skoru, 97.89’luk bir hassasiyet (Precision) değeri ve 97.47’lik bir hatırlama (Recall) değeri elde edilmiştir. Xception ağının 200X yakınlaştırma oranındaki ROC eğrisi altındaki alan değeri ise 0.975 olarak hesaplanmıştır.

Keywords

• Meme kanseri
• Histopatolojik görüntü
• Sınıflandırma
• Derin sinir ağları
• Xception
• ön-eğitimli ağlar

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