Medical image classification with hybrid convolutional neural network models

Abstract

Despite important developments in medicine and technology today, many people die due to false or late diagnosis. It is very important to identify the small details in the images that can be overlooked in the examinations made on medical images in terms of early diagnosis of the disease. Therefore, it is vital in some cases to provide early diagnosis by detecting the details in the images automatically by computer systems. In the study carried out, it was aimed to diagnose the disease through medical images by classifying different types of images. For this purpose, convolutional neural networks, which are among deep learning techniques, were evaluated together with different classifier models. In the applied hybrid model approach, feature extraction was obtained from medical images with the convolutional neural network model. The extracted features are used to train different classification models. In the continuation of the study, the performance results obtained from the classifier models are compared. Two different datasets including brain MR images and lung x-ray images were used in the training and testing of hybrid models. In the study, images were classified into two categories as malignant and benign tumors in order to detect images containing malignant tumors in MR images. In order to identify images with pneumonia, the images are similarly classified into two categories, healthy and pneumonia. At the end of the study, the performance results obtained from the model approaches were compared and the performance evaluation of the models was performed.

Keywords

• Deep learning,Medical diagnosis,Convolutional neural network,Data augmentation,Classification

Hibrit evrişimli sinir ağı modelleri ile tıbbi görüntü sınıflandırması

Abstract

Günümüzde tıp ve teknolojideki önemli gelişmelere rağmen, birçok kişi yanlış veya geç tanı nedeniyle hayatını kaybetmektedir. Tıbbi görüntüler üzerinde yapılan muayenelerde hastalığın erken teşhisi açısından gözden kaçırılabilecek görüntülerdeki küçük detayların belirlenmesi çok önemlidir. Bu nedenle, bazı durumlarda görüntülerdeki detayları bilgisayar sistemleri tarafından otomatik olarak tespit ederek erken teşhis sağlamak hayati önem taşımaktadır. Yapılan çalışmada, farklı görüntü tiplerini sınıflandırarak hastalığın tıbbi görüntülerle teşhis edilmesi amaçlanmıştır. Bu amaçla, derin öğrenme teknikleri arasında yer alan evrişimli sinir ağları, farklı sınıflayıcı modellerle birlikte değerlendirilmiştir. Uygulanan hibrid model yaklaşımında, evrişimli sinir ağı modeli ile tıbbi görüntülerden özellik çıkarımı elde edilmiştir. Çıkarılan özellikler farklı sınıflandırma modellerini eğitmek için kullanılır. Çalışmanın devamında, sınıflandırıcı modellerinden elde edilen performans sonuçları karşılaştırılmıştır. Hibrid modellerin eğitim ve testinde beyin MR görüntüleri ve akciğer röntgeni görüntüleri dahil olmak üzere iki farklı veri seti kullanılmıştır. Çalışmada MR görüntülerinde malign tümör içeren görüntüleri saptamak için görüntüler malign ve benign tümörler olarak iki kategoriye ayrıldı. Akciğer iltihaplanmalı görüntüleri tanımlamak için görüntüler benzer şekilde sağlıklı ve akciğer iltihaplanması olmak üzere iki kategoriye ayrılır. Araştırma sonunda model yaklaşımlarından elde edilen performans sonuçları karşılaştırılmış ve modellerin performans değerlendirmesi yapılmıştır.

Keywords

• Derin öğrenme,Tıbbi teşhis,Evrişimli sinir ağı,Veri artırma,Sınıflandırma

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