Research Article


DOI :10.26650/acin.880918   IUP :10.26650/acin.880918    Full Text (PDF)

Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images

Onur Sevli

A brain tumor is a collection of abnormal cells formed as a result of uncontrolled cell division. If tumors are not diagnosed in a timely and accurate manner, they can cause fatal consequences. One of the commonly used techniques to detect brain tumors is magnetic resonance imaging (MRI). MRI provides easy detection of abnormalities in the brain with its high resolution. MR images have traditionally been studied and interpreted by radiologists. However, with the development of technology, it becomes more difficult to interpret large amounts of data produced in reasonable periods. Therefore, the development of computerized semi-automatic or automatic methods has become an important research topic. Machine learning methods that can predict by learning from data are widely used in this field. However, the extraction of image features requires special engineering in the machine learning process. Deep learning, a sub-branch of machine learning, allows us to automatically discover the complex hierarchy in the data and eliminates the limitations of machine learning. Transfer learning is to transfer the knowledge of a pre-trained neural network to a similar model in case of limited training data or the goal of reducing the workload. In this study, the performance of the pre-trained Vgg-16, ResNet50, Inception v3 models in classifying 253 brain MR images were evaluated. The Vgg-16 model showed the highest success with 94.42% accuracy, 83.86% recall, 100% precision and 91.22% F1 score. This was followed by the ResNet50 model with an accuracy of 82.49%.The findings obtained in this study were compared with similar studies in the literature and it was found that it showed higher success than most studies.

DOI :10.26650/acin.880918   IUP :10.26650/acin.880918    Full Text (PDF)

Beyin MR Görüntülerini Sınıflandırmada Farklı Önceden Eğitilmiş Derin Öğrenme Modellerinin Performans Karşılaştırması

Onur Sevli

Beyin tümörleri, beyin hücrelerinin kontrolsüz bölünmeleri sonucu meydana gelen kitlelerdir. Tümörler zamanında ve doğru teşhis edilmezlerse ölümcül sonuçlara neden olabilir. Beyin tümörlerini tespit etmede yaygın olarak kullanılan tekniklerden biri olan MRI’dir. MRI, sağladığı yüksek çözünürlük ile beyindeki anormalliklerin kolay tespitine imkân verir. MR görüntüleri geleneksel olarak radyologlar tarafından incelenip yorumlanır. Ancak teknolojinin gelişmesi ile birlikte üretilen çok miktarda veriyi makul sürelerde yorumlamak daha zor hale gelmektedir. Bu nedenle bilgisayarlı yarı otomatik ya da otomatik yöntemlerin geliştirilmesi önemli bir araştırma konusu haline gelmiştir. Verilerden öğrenerek tahmin yapabilen makine öğrenmesi yöntemleri bu alanda yaygın olarak kullanılmaktadır. Ancak makine öğrenmesi için görüntü özelliklerinin çıkarımı özel bir mühendislik gerektirir. Makine öğrenmesinin bir alt dalı olan derin öğrenme, veri içerisindeki karmaşık hiyerarşiyi otomatik olarak keşfetmeye imkân sağlar ve makine öğrenmesinin sınırlılıklarını ortadan kaldırır. Transfer öğrenme ise eldeki eğitim verisinin az olması halinde ya da iş yükünü azaltmak için daha önceden eğitilmiş bir derin sinir ağının bilgilerinin benzer başka bir modele aktarılmasıdır. Bu çalışmada önceden eğitilmiş Vgg-16, ResNet50 ve Inception v3 modellerinin sınıflamadaki performansları değerlendirilmiştir. Vgg-16 modeli %94.42 doğruluk, %83.86 recall, %100 precision ve %91.22 F1 skoru ile en yüksek başarıyı göstermiştir. Bunu %82.49 doğrulukla ResNet50 modeli izlemektedir. Bu çalışmada elde edilen bulgular literatürdeki benzer çalışmalarla karşılaştırılmış ve çoğu çalışmadan daha yüksek başarı gösterdiği görülmüştür. 


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APA

Sevli, O. (2021). Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images. Acta Infologica, 5(1), 141-154. https://doi.org/10.26650/acin.880918


AMA

Sevli O. Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images. Acta Infologica. 2021;5(1):141-154. https://doi.org/10.26650/acin.880918


ABNT

Sevli, O. Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images. Acta Infologica, [Publisher Location], v. 5, n. 1, p. 141-154, 2021.


Chicago: Author-Date Style

Sevli, Onur,. 2021. “Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images.” Acta Infologica 5, no. 1: 141-154. https://doi.org/10.26650/acin.880918


Chicago: Humanities Style

Sevli, Onur,. Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images.” Acta Infologica 5, no. 1 (Dec. 2021): 141-154. https://doi.org/10.26650/acin.880918


Harvard: Australian Style

Sevli, O 2021, 'Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images', Acta Infologica, vol. 5, no. 1, pp. 141-154, viewed 6 Dec. 2021, https://doi.org/10.26650/acin.880918


Harvard: Author-Date Style

Sevli, O. (2021) ‘Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images’, Acta Infologica, 5(1), pp. 141-154. https://doi.org/10.26650/acin.880918 (6 Dec. 2021).


MLA

Sevli, Onur,. Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images.” Acta Infologica, vol. 5, no. 1, 2021, pp. 141-154. [Database Container], https://doi.org/10.26650/acin.880918


Vancouver

Sevli O. Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images. Acta Infologica [Internet]. 6 Dec. 2021 [cited 6 Dec. 2021];5(1):141-154. Available from: https://doi.org/10.26650/acin.880918 doi: 10.26650/acin.880918


ISNAD

Sevli, Onur. Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images”. Acta Infologica 5/1 (Dec. 2021): 141-154. https://doi.org/10.26650/acin.880918



TIMELINE


Submitted15.02.2021
Accepted05.05.2021
Published Online29.07.2021

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