ÖNCEDEN EĞİTİLMİŞ DERİN AĞLAR İLE GÖĞÜS RÖNTGENİ GÖRÜNTÜLERİ KULLANARAK PNÖMONİ SINIFLANDIRILMASI

Pnömoni, bakterilerin, virüslerin veya mantarların neden olabileceği bir akciğer enfeksiyonudur.Enfeksiyon, akciğerlerin hava keselerinin iltihaplanmasına ve sıvı veya irin ile dolmasına neden olur.Ciddi ve hayatı tehdit eden bir hastalık olabilir. Dünya genelinde her yıl pnömoni nedeniyle çok sayıdakişi ölmektedir. Pnömoninin erken tespiti ve tedavisi, ölüm oranlarını önemli ölçüde azaltabilmektedir.Bu nedenle, bu araştırmada pnömoniyi tespit etmek için röntgen görüntüleri kullanarak önceden eğitilmişderin öğrenme modellerine dayanan yöntem önerilmektir. Göğüs röntgen görüntülerini pnömoni vepnömoni olmayan iki sınıfta sınıflandırmak için çeşitli önceden eğitilmiş Evrişimsel Sinir Ağları özellikçıkarıcı olarak kullanılmıştır. Önceden eğitilmiş derin öğrenme modelleri olarak AlexNet, VGG16, ResNet(ResNet18, ResNet50, ResNet101) modelleri tercih edilmiştir. Bu modellerden elde edilen özelliklerbirleştirilerek hibrit özellik vektörü elde edilmiştir. Sınıflandırıcı olarak Destek Vektör Makineleri (DVM)ve derin öğrenme modellerinin son katmanında bulunan Softmax kullanılmıştır. Deneyler literatürdeyaygın kullanılan veri seti üzerinde yapılmıştır. En yüksek sınıflandırma başarısı %98,32 olarak hibritözellik vektöründen elde edilmiştir.

Classification of Pneumonia Using Pre-Trained Deep Networks with Chest X-Ray Images

Pneumonia is a lung infection that can be caused by bacteria, viruses, or fungi. The infection causes the lungs to become inflamed and filled with fluid or pus. It can be a serious and life-threatening disease. Many people die every year due to pneumonia worldwide. Early detection and treatment of pneumonia can significantly reduce mortality. For this reason, this research is to propose a method based on pre-trained deep network models using x-ray images to detect pneumonia. Various pre-trained Convolutional Neural Networks were used as feature extractors to classify chest x-ray images into two classes without pneumonia and pneumonia. AlexNet, VGG16, ResNet (ResNet18, ResNet50, ResNet101) models are preferred as pre-trained deep network models. The hybrid feature vector is obtained by combining the features obtained from these models. As the classifier, Support Vector Machines (SVM) and Softmax in the last layer of deep networks are used. Experiments are carried out on the data set commonly used in the literature. The highest classification success is obtained from the hybrid feature vector as 98.32%.

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