2016

MR Spektroskopi Sinyalleri Kullanılarak LSTM Derin Sinir Ağları ile Beyinde Sahte Tümörlerin Tespiti

Manyetik rezonans spektroskopi (MRS) günümüzde beyin tümörlerinin tespitinde kullanılan müdahalesiz araçlardan biridir. Biyopsi gibi ameliyata bağlı enfeksiyon ve ölüm riski getirmediği için hekimler tarafından yaygın olarak tercih edilmektedir. MRS beyinle ilgili metabolik bir profil sunmaktadır. Tümör ve sahte tümörlerin MRS örüntüleri birbirleri ile benzerlik gösterebilmektedir. Bu sebepten dolayı beyin tümörünün doğru teşhisi ve sınıflandırılması, hastanın tedavi planlaması açısından hayati bir önem taşımaktadır. Bu çalışmada, MRS verileri kullanılarak, derin sinir ağları ile gerçek ve sahte beyin tümörlerinin sınıflandırılması sağlanmıştır. Çalışma kapsamında yürütülen deneysel çalışmalarda, LSTM (Long Short Term Memory – Uzun Kısa Süreli Bellek) ve Bi-LSTM (Bi-directional Long Short Term Memory – Çift Yönlü Uzun Kısa Süreli Bellek) derin sinir ağları mimarileri kullanılmıştır. Çalışmada INTERPRET veritabanında bulunan tümör ve sahte tümörlere ait MRS sinyal örüntüleri kullanılmıştır. LSTM sinir ağlarının eğitimi ve test edilmesi için çok sayıda tümör ve sahte tümöre ait MRS verisini elde etmek gerçek dünyada zor bir prosedürel süreç olduğundan, ağ eğitilmeden ve test edilmeden önce, MRS veriseti için veri büyütme (çoğaltma) yöntemleri ile veri sayısı çoğaltılmıştır. LSTM sinir ağları, hem veri çoğaltma olmadan hem de veri çoğaltma ile eğitilmiş ve test edilmiştir. Kullanılan LSTM sinir ağlarının eğitim ve testleri esnasında her model için tekrarlı K-kat çapraz doğrulama yöntemi kullanılmıştır. Eğitimler, her model için 5 kat ve 10 tekrar ile yapılmıştır. MRS verilerini bilgisayar destekli sınıflandırmaya dayalı bir yöntem ile sınıflandıran bu çalışma sonucunda, geliştirilen uygulama ile veri çoğaltma olmadan yapılan testlerde, kullanılan iki mimari için ortalama %81.15 doğruluk başarımı elde edilirken; veri çoğaltma yapıldıktan sonra yapılan testlerde, her iki mimari için ortalama %95.15 doğruluk başarımı elde edilmiştir.

Anahtar Kelimeler:

Beyin tümörleri, MRS, Sınıflandırma, Derin Öğrenme, LSTM, Bi-LSTM

Detection of Pseudo Brain Tumors via LSTM Neural Networks using MR Spectroscopy Signals

Magnetic resonance spectroscopy (MRS) is one of the non-invasive tools used in the detection of brain tumors today. It is widely preferred by physicians because it does not pose a risk of surgical infection and death such as biopsy. MRS provides a metabolic profile about the brain. MRS patterns of tumors and pseudo tumors can be similar to each other. For this reason, accurate diagnosis and classification of the brain tumor is vital for the treatment of the patient. In this study, the classification of real and pseudo brain tumors with deep neural networks was provided by using MRS data. In experimental studies carried out within the scope of the study, LSTM (Long Short Term Memory) and Bi-LSTM (Bidirectional Long Short Term Memory) deep neural network architectures were used. In the study, MRS signal patterns of tumors and pseudo tumors in the INTERPRET database were used. Since obtaining MRS data from a large number of tumors and pseudo tumors for the training and testing of LSTM neural networks is a difficult procedural process in the real world, the number of data has been increased by MRS data augmentation (replication) methods before the network is trained and tested. LSTM neural networks are trained and tested both with and without data augmentation methods. During the training and testing of the LSTM neural networks, repeated K-fold cross-validation method was used for each model. Neural network trainings were carried out with 5 folds and 10 repetitions for each model. As a result of this study which classifies MRS data with a method based on computer-aided classification; in the tests carried out without data augmentation with the developed application, an average of 81.15% accuracy was achieved for the 2 neural network architectures while in the tests performed after data augmentation, an average of 95.15% accuracy performance was achieved for both networks.

Keywords:

Brain Tumors, MRS, Classification, Deep Learning, LSTM, Bi-LSTM,

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