Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması

Year 2021, Volume: 36 Issue: 2, 997 - 1012, 05.03.2021

Kali Gurkahraman , Rukiye Karakış

https://doi.org/10.17341/gazimmfd.762056

Cited By: 7

Abstract

Tıbbi görüntü sınıflandırma, veriyi istenilen sayıda sınıfa ayrıştırma işlemidir. Son yıllarda, Manyetik Rezonans Görüntüleme (MRG) beyin tümörlerinin tespit edilmesinde ve tanısında yaygın olarak kullanılmaktadır. Bu çalışmada, üç farklı beyin tümörünün(gliyom, menenjiyom ve hipofiz bezesi) T1 ağırlıklı MR görüntüleri üzerinde evrişimsel sinir ağı (ESA) kullanılarak sınıflandırılması ve aksiyel, koronel ve sagital MR kesitlerinin sınıflandırmadaki etkinliğinin belirlenmesi amaçlanmıştır. Ağırlıklar, başlangıçta ImageNet veri kümesi için eğitilmiş DenseNet121 ağından ESA’ya transfer edilmiştir. Ayrıca, afin dönüşümü ve piksel-seviye dönüşümü MR görüntülerinde veri çoğaltmada kullanılmıştır. Eğitilen ESA’nın tam bağlantılı ilk katmanından elde edilen öznitelikler, destek vektör makinesi(DVM), k en yakın komşu (kNN) ve Bayes yöntemleriyle de sınıflandırılmıştır. Bu sınıflandırıcıların başarısı test veriseti üzerinde duyarlılık, belirlilik, doğruluk, eğri altında kalan alan ve korelasyon katsayısı ile ölçülmüştür. ESA, ve ESA tabanlı DVM, kNN ve Bayes sınıflandırıcılarının elde ettiği doğruluk değerleri sırasıyla 0.9860, 0.9979, 0.9907 ve 0.8933’ dür. Beyin tümör sınıflandırma için önerilen ESA tabanlı DVM modeli literatürdeki benzer çalışmalardan daha yüksek performans değerleri elde etmiştir. Ayrıca beyin tümör tipini görüntülerden belirlemede beyin koronel kesitleri diğer kesitlere göre daha etkindir.

Keywords

Beyin Tümör Sınıflandırma, Derin Öğrenme, Destek Vektör Makineleri, Öznitelik Çıkarımı, Veri Çoğaltma

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