Kürşat DEMİR, Berna ARI, Fatih DEMİR

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MR görüntüleri kullanılarak öznitelik seçimine dayalı ön-eğitilmiş bir derin öğrenme modeliyle beyin tümörünün tespiti

Dünyadaki en tehlikeli hastalıklardan biri beyin tümörüdür. Bir beyin tümörü beyindeki sağlıklı dokuyu yok eder ve daha sonra anormal şekilde çoğalarak kafatasında iç basıncın artmasına neden olur. Bu erken teşhis edilmezse ölüme yol açabilir. Manyetik Rezonans Görüntüleme (MRG) yumuşak dokularda sıklıkla kullanılan ve başarılı sonuçlar veren bir tanı yöntemidir. Bu çalışmada, MR görüntülerinden bir beyin tümörü otomatik olarak tespit edildi. Öznitelik çıkarımı için MobilenetV2 adlı önceden eğitilmiş bir Evrişimsel Sinir Ağı modeli kullanılmıştır. Daha sonra öznitelik seçimi için ReliefF algoritması kullanılmıştır. MobileNetV2 ile çıkarılan öznitelikler ve ReliefF algoritması ile seçilen öznitelikler ayrı ayrı sınıflandırıcılara verilerek sistem performansı test edilmiştir. Deneysel çalışmalar sonucu MobileNetV2 öznitelik çıkarımı, ReliefF algoritması öznitelik seçimi ve KNN sınıflandırıcı kombinasyonuyla en yüksek başarımın elde edildiği görülmüştür.
Anahtar Kelimeler:

Öznitelik seçimi, ReliefF algoritması, Beyin tümörü, Manyetik rezonans görüntüleri

Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

One of the most dangerous diseases in the world is a brain tumor. A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull. This can lead to death if not diagnosed early. Magnetic Resonance Imaging (MRI) is a diagnostic method that is frequently used in soft tissues and gives successful results. In this study, a brain tumor was automatically detected from MR images. For feature extraction, a pre-trained Convolutional Neural Network (CNN) model named MobilenetV2 was used. Then, the ReliefF algorithm was used for feature selection. The features extracted with MobileNetV2 and the features selected with the ReliefF algorithm are given separately to the classifiers and the system performance is tested. As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.
Keywords:

Feature selection, ReliefF algorithm, MobileNetV2, brain tumor, Magnetic resonance imaging Öznitelik seçimi, ReliefF algoritması, Beyin tümörü, Manyetik rezonans görüntüleri,

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Firat University Journal of Experimental and Computational Engineering-Cover
  • Başlangıç: 2022
  • Yayıncı: Fırat Üniversitesi
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