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Akciğer Histopatoloji Görüntülerinden Çıkarılan Derin Özellikleri Kullanan Makine Öğrenmesi Sınıflandırıcıları ile Akciğer Kanseri Tespiti

Kanser dünyada ve ülkemizde gözlenme sıklığı giderek artan sağlık sorunlarının başında gelmekte ve her yıl milyonlarca insan kanser nedeniyle hayatını kaybetmektedir. Histopatolojik tanı, kanser türünün teşhisinde ve tedavi stratejisinin belirlenmesinde önemli bir rol oynamaktadır. Bu çalışmada akciğer histopatoloji görüntüleri kullanılarak derin öğrenme yöntemlerine dayalı bir otomatik model geliştirilmesi amaçlanmıştır. Geliştirilen modelde öncelikle DenseNet201, MobileNetV2, VGG16, NASNetLarge, Xception, InceptionV3, VGG19, EfficientNetB7 ve ResNet152 gibi önceden eğitilmiş derin öğrenme mimarileri kullanılarak özellik çıkarımı gerçekleştirilmiş ve daha sonra Adaboost, Çok katmanlı algılayıcı, Rastgele orman ve Destek vektör makinesi gibi makine öğrenmesi yöntemleri ile sınıflandırılmıştır. Ardından sınıflandırıcılardan elde edilen değerlendirme sonuçlarına göre en iyi performansa sahip ilk üç derin öznitelik birleştirilerek makine öğrenmesi sınıflandırıcılarına girdi olarak kullanılmıştır. Deneysel sonuçlar en iyi özniteliklerin birlikte kullanılmasının sınıflandırma başarısına olumlu yönde katkı sağladığını göstermiştir. Test veri setinden elde edilen sonuçlar, önerilen hibrit yaklaşımın%97.22 ortalama sınıflandırma başarısı ile akciğer histopatoloji görüntülerinden adenokarsinom, skuamöz hücreli karsinom ve normal dokuların otomatik sınıflandırmasında etkili olduğunu göstermiştir.

Lung Cancer Detection with Machine Learning Classifiers using Deep Features Extracted from Lung Histopathology Images

Cancer is one of the health problems with an increasing incidence in the world and in our country, and millions of people die every year due to cancer. Histopathological diagnosis plays an important role in diagnosing the type of cancer and determining the treatment strategy. In this study, it is aimed to develop an automatic model based on deep learning methods using lung histopathology images. In the developed model, firstly feature extraction was performed using pre-trained deep learning architectures such as DenseNet201, MobileNetV2, VGG16, NASNetLarge, Xception, InceptionV3, VGG19, EfficientNetB7 and ResNet152, and then classified with machine learning methods such as Adaboost, Multi-layer perceptron, Random forest and Support vector machines. Afterwards, according to the evaluation results obtained from the classifiers, the first three deep features with the best performance were combined and used as input to the machine learning classifiers. Experimental results showed that using the best features together contributes positively to the classification success. Results from the test dataset showed that the proposed hybrid approach was effective in automatic classification of adenocarcinoma, squamous cell carcinoma and benign tissues from lung histopathology images, with an average classification accuracy of 97.22%.

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