Geliştirilmiş bir web tabanlı arayüz kullanarak beyin tümörlerinin manyetik rezonans görüntülerinde derin öğrenme tabanlı modellerle otomatik sınıflandırılması

Year 2021, Volume: 13 Issue: 2, 192 - 200, 07.06.2021

Bora Tetik , Hasan Ucuzal , Şeyma Yaşar , Cemil Çolak

https://doi.org/10.18521/ktd.889777

Abstract

Amaç: Primer santral sinir sistemi tümörleri (PSSST), dünyada yeni teşhis edilen kanserlerin yaklaşık %3'ünü oluşturmaktadır ve erkeklerde sıklığı daha yüksektir. Beyin tümörlerinin ve PSSST'lere bağlı ölümlerin görülme sıklığı tüm dünyada giderek artmaktadır. Son zamanlarda birçok çalışma, tıbbi görüntüleme uygulamalarında derin öğrenme algoritmaları kullanılarak geliştirilen otomatik makine öğrenimi (AutoML) algoritmalarına odaklanmıştır. Bu çalışmanın temel amacı, radyologlara destek sağlamak için beyin tümörlerinin (glioma, menenjiom hipofiz adenomları) tıbbi görüntülerinin analizinde yapay zeka tabanlı tekniklerin kullanımını göstermek, hızlı ve doğru tanı konulması için beyin tümörlerini sınıflandıran kullanıcı dostu ve ‘ücretsiz web tabanli bir yazılım geliştirmektir.

Gereç ve Yöntemler: Açık kaynaklı T1 ağırlıklı manyetik rezonans beyin tümörü görüntüleri Nanfang Hastanesi, Guangzhou, Çin ve Genel Hastane, Tianjin Tıp Üniversitesinden elde edildi. Önerilen web tabanlı arayüzün ve derin öğrenme tabanlı modellerin oluşturulması için Python'un programlama dilinde kullanılan Keras / Auto-Keras kütüphanesi kullanıldı. Performans değerlendirmelerinde doğruluk, duyarlılık, özgüllük, G-ortalama, F-skor ve Matthews korelasyon katsayısı ölçümleri kullanıldı.

Sonuçlar: Eğitim aşamasında veri kümesinin %80'i (2599 örnek) kullanılırken, %20'si (465 örnek) test aşamasında kullanıldı. Eğitim veri setinde beyin tümörlerinin sınıflandırılmasında tüm performans ölçütleri %98'in üzerinde sonuçlanmıştır. Benzer şekilde, test veri setinde menenjiom için duyarlılık ve MCC dışındaki tüm değerlendirme ölçütleri % 91'den yüksektir.

Sonuç: Deneysel sonuçlar, önerilen yazılımın üç tip beyin tümörünü tespit etmek ve tanı koymak için kullanılabileceğini ortaya koymaktadır. Geliştirilen bu web tabanlı yazılıma hem İngilizce hem de Türkçe olarak http://biostatapps.inonu.edu.tr/BTSY/ adresinden ücretsiz olarak erişilebilir.

Keywords

Beyin tümörleri, Derin öğrenme yaklaşımları, Keras/Auto-Keras, T1 ağırlıklı manyetik rezonans görüntüleme

Automated classification of brain tumors by deep learning-based models on magnetic resonance images using a developed web-based interface

Abstract

Objective: Primary central nervous system tumors (PCNSTs) compose nearly 3% of newly diagnosed cancers worldwide and are more common in men. The incidence of brain tumors and PCNSTs-related deaths are gradually increasing all over the world. Recently, many studies have focused on automated machine learning (AutoML) algorithms which are developed using deep learning algorithms on medical imaging applications. The main purposes of this study are -to demonstrate the use of artificial intelligence-based techniques to predict medical images of different brain tumors (glioma, meningioma, pituitary adenoma) to provide technical support to radiologists, and -to develop a user-friendly and free web-based software to classify brain tumors for making quick and accurate clinical decisions.

Materials and Methods: Open-sourced T1-weighted magnetic resonance brain tumor images were achieved from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, To construct the proposed system which web-based interface and the deep learning-based models, the Keras/Auto-Keras library, which is employed in Python's programming language, is used. Accuracy, sensitivity, specificity, G-mean, F-score, and Matthews correlation coefficient metrics were used for performance evaluations.

Results: While 80% (2599 instances) of the dataset was used in the training phase, 20% (465 instances) was employed in the testing phase. All the performance metrics were higher than 98% for the classification of brain tumors on the training data set. Similarly, all the evaluation metrics were higher than 91% except for sensitivity and MCC for meningioma on the testing dataset.

Conclusion: The results from the experiment reveal that the proposed software can be used to detect and diagnose three types of brain tumors. This developed web-based software can be accessed freely in both English and Turkish at http://biostatapps.inonu.edu.tr/BTSY/.

Keywords

Brain tumors, deep-learning strategy, Keras/Auto-Keras, T1-weighted magnetic resonance imaging

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