Patoloji Görüntülerinin Derin Öğrenme Yöntemleri İle Sınıflandırılması

Öz

Meme kanseri, kadınlar arasında en çok görülen kanser türüdür. Kanserin erken tespit edilmesi, tedavinin zamanında yapılmasını sağlamaktadır. 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 yöntemi kullanılmaktadır. Bu yöntemlerle görüntülerden anlamlı bilgiler çıkarılarak anormalliklerin tespit edilmesi veya tahmin edilmesi işlemleri yapılmaktadır. Yapay zeka yöntemlerinden biri olan derin öğrenme, patoloji 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, patoloji görüntülerinin analizi için en çok tercih edilen yöntemlerden biri haline gelmiştir. Bu çalışmada, derin öğrenme mimarilerinden evrişimsel sinir ağı (Convolutional Neural Networks, CNN) kullanılarak, meme kanserine ilişkin patolojik görüntülerin sınıflandırması işlemi gerçekleştirilmiştir. Farklı sayıda filtre ve katman kullanılarak tasarlanan modellerin eğitimi ve test işlemleri için 60000 adet meme patoloji görüntüsünden oluşan bir veri seti kullanılmıştır. Model eğitimleri için donanım olarak Google Colab üzerinde NVIDIA Tesla K80 GPU işlemcili bir makine kullanılmış olup yazılım aracı olarak açık kaynak kodlu Keras kütüphanesi ve Python programlama dili kullanılmıştır. 3 adet evrişim katmanı, 3 adet ReLU katmanı, 3 adet havuzlama katmanı ve tam bağlantılı katmanda 200 sinir hücresi kullanılarak eğitilen model ile kanserli ve kanserli olmayan patoloji görüntülerinin sınıflandırılmasında doğruluk değeri 0.8775, F1 skoru 0.8238, hassasiyet değeri 0.8381, hatırlama değeri 0.8762, MSE değeri 0.1195, MAE değeri 0.2497 elde edilmiştir. Elde edilen yüksek doğruluk, F1 skoru, hassasiyet ve hatırlama değerleri ile düşük hata değerleri, bu tez kapsamında önerilen CNN modelinin patoloji görüntülerinin sınıflandırılmasında kullanılabileceğini; özellikle tıp fakültelerinin ve hastanelerin patoloji bölümlerinde kullanılabilir sistemler tasarlanabileceğini göstermektedir.

Anahtar Kelimeler

• Patoloji Görüntüleri
• Özellik Çıkarımı
• Derin Öğrenme
• Evrişimsel Sinir Ağları
• Sınıflandırma.

Classification Of Pathology Images Using Deep Learning Methods

Öz

Breast cancer is the most common type of cancer among women. Early detection of cancer ensures timely treatment. Medical images are of vital importance in the detection of diseases existing in the bodies of living things or in the early diagnosis of diseases that may occur. Pathology images, which are especially used for cancer detection, are a type of medical image that is transferred to the computer environment by dipping a part of a patient into various chemical substances and scanning with special machines. Many machine learning and artificial intelligence methods are used to analyze medical images. With these methods, meaningful information is extracted from the images and anomalies are detected or predicted. Deep learning, one of the artificial intelligence methods, shows significant success in analyzing pathology images. Deep learning architectures perform feature extraction in machine learning studies with convolution layers within the architecture. Deep learning algorithms used for operations such as image classification, object recognition and segmentation have become one of the most preferred methods for the analysis of pathology images. In this study, classification of pathological images related to breast cancer was carried out by using convolutional neural network (Convolutional Neural Networks, CNN), which is one of the deep learning architectures. A data set consisting of 60000 breast pathology images was used for training and testing of models designed using different numbers of filters and layers. For model trainings, a machine with NVIDIA Tesla K80 GPU processor on Google Colab was used as hardware, and the open source Keras library and Python programming language were used as software tools. The model, which was trained by using 3 convolution layers, 3 ReLU layers, 3 pooling layers and 200 neurons in a fully connected layer, obtained 0.8775 accuracy value, 0.8238 F1 score, 0.8381 precision value, 0.8762 recall value, 0.1195 MSE value and 0.2497 MAE value in the classification of cancerous and non-cancerous pathology images. Obtained high accuracy, F1 score, precision and recall values with low error values, can be used in the classification of pathology images of the proposed CNN model within the scope of this thesis; shows usable systems can be designed especially in the pathology departments of medical faculties and hospitals.

Anahtar Kelimeler

• Breast Cancer
• Pathology Images
• Feature Extraction
• Deep Learning
• Convolutional Neural Networks
• Keras
• Classification..

Kaynakça

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