Cancer is the second most common cause of human death. Nowadays, the rate of people getting cancer is increasing.Breast cancer is the most common type of cancer among women. The exact causes of the disease are not known. Medical images are vital in detecting diseases that exist in the bodies of living things or in the early diagnosis of diseases that may occur. Particularly used for cancer detection, the pathology images are a type of medical image which is stained into a chemical material of a piece taken from the patient and scanned with special machines and transferred to computer. Many machine learning and artificial intelligence methods are used for the analysis of medical images. With these methods, significant information is extracted from the images to detect or predict abnormalities. In recent years, deep learning, which is one of the methods of artificial intelligence, has shown significant success in analyzing the pathological images of medical images. Deep learning architectures perform feature extraction in machine learning studies with the convolution layers within the architecture. Deep learning algorithms used for image classification, object recognition and segmentation have become one of the most preferred methods for the analysis of pathology images. In this thesis, an architecture has been developed by using CNN which is the most successful deep learning method. Of the 60000 breast pathology images, 50000 were devoted to training and 10000 for testing. A classification was performed on the NVIDIA Tesla K80 GPU accelerator using the Keras library and Python programming language and 0.8824 accuracy value, 0.0806 MSE value and 0.3363 MAE value was obtained. Kanser, insan ölümlerine sebep olan hastalıklar arasında ikinci sırada gelmektedir. Günümüzde insanların kansere yakalanma oranları giderek artmaktadır. Kanser; kötü beslenme, sigara ve alkol kullanımı, uzun süre güneş ışığına maruz kalma gibi birçok nedenle ortaya çıkmaktadır. Meme kanseri, kadınlar arasında en çok görülen kanser türüdür. Medikal görüntüler, canlıların vücutlarında var olan hastalıkların tespitinde veya meydana gelebilecek olan hastalıkların erken tanısında hayati öneme sahiptir. Özellikle kanser tespiti yapmak amacıyla kullanılan patoloji görüntüleri, hastadan alınan bir parçanın çeşitli kimyasal maddelere batırılıp özel makinelerle taranarak bilgisayar ortamına aktarılan medikal görüntü çeşididir. Medikal görüntülerin analiz edilmesi için birçok makine öğrenmesi ve yapay zeka yöntemleri kullanılmaktadır. Bu yöntemlerle görüntülerden anlamlı bilgiler çıkarılarak anormalliklerin tespit edilmesi veya tahmin edilmesi işlemleri yapılmaktadır. Son yıllarda yapay zeka yöntemlerinden birisi olan derin öğrenme, medikal görüntülerden olan pataloji görüntülerinin analiz edilmesinde önemli başarılar göstermektedir. Derin öğrenme mimarileri, makine öğrenimi çalışmalarındaki özellik çıkarım işlemini mimari içerisinde yer alan evrişim katmanları ile yapmaktadır. Görüntü sınıflandırma, nesne tanıma, segmentasyon gibi işlemler için kullanılan derin öğrenme algoritmaları, pataloji görüntülerinin analizi için en çok tercih edilen yöntemlerden biri haline gelmiştir. Bu tez çalışmasında, en başarılı derin öğrenme yöntemi olan evrişimel sinir ağları (CNN) kullanılarak bir mimari geliştirilmiştir. 60000 tane meme patoloji görüntüsünden oluşan bir veri setinin 50000 tanesi eğitim ve 10000 tanesi test için ayrılmıştır. NVIDIA Tesla K80 GPU hızlandırıcı üzerinde Keras kütüphanesi ve Python programlama dili kullanılarak bir sınıflandırma işlemi yapılmıştır ve 0.8824 oranında doğruluk değeri, 0.0806 MSE değeri ve 0.3363 MAE değeri elde edilmiştir.

Keywords - Breast Cancer, Pathology Images, Deep Learning, CNN, Keras, Classification

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