CLASSIFICATION OF BRAIN TUMORS ON MRI IMAGES USING DEEP LEARNING ARCHITECTURES

Samaneh Sarfarazı; Önsen Toygar

doi:10.17780/ksujes.1339884

TR EN

DERİN ÖĞRENME MİMARİLERİ KULLANILARAK MRI GÖRÜNTÜLERİ ÜZERİNDE BEYİN TÜMÖRÜ SINIFLANDIRMASI

Öz

Beyin tümörü, beyindeki veya kafadaki hapishane hücresinin katı bir şekilde büyümesiyle üretilen tehlikeli bir nöral hastalıktır. Manyetik Rezonans Görüntüleme (MRI) görüntülerinden temiz olmayan tümör parçalarının segmentasyonu, analizi ve ayrılması kaygının ana kaynağıdır. Tümör içeren MRI görüntülerinin raporlanabilmesi için bilgisayar destekli yöntemlerin kullanılması gerekli hale gelmiştir. Bu makalede, MRI görüntülerinde beyin tümörlerini tanımlamak için Evrişimli Sinir Ağları (CNN) yaklaşımı kullanılmıştır. Bu çalışma için Kaggle Brain MRI veri kümesi ve Figshare Brain MRI veri kümesi olmak üzere iki veri kümesi kullanılmıştır. Derin öznitelikleri çıkarmak için VGG16, AlexNet ve ResNet'ten oluşan derin CNN modelleri kullanılmıştır. Söz konusu Derin Öğrenme (DL) modellerinin sınıflandırma doğrulukları, uygulanan sistemlerin verimliliklerini ölçmek için kullanılmıştır. Kaggle veri kümesi için AlexNet %98, VGG16 %97 ve ResNet %66 doğruluk elde etmiştir. Bu ağlar arasında AlexNet en yüksek düzeyde doğruluk sağlamıştır. Figshare veri kümesinde ise, AlexNet ve VGG16'nın her ikisi de %99, ResNet ise %96 doğruluk elde etmiştir. Doğruluk açısından AlexNet ve VGG16, ResNet'ten daha iyi performans göstermiştir. Bu performanslar, kanserlerin felç ve diğer komplikasyonlar gibi fiziksel zararlara yol açmadan önce erken teşhis edilmesine yardımcı olacaktır.

Anahtar Kelimeler

CLASSIFICATION OF BRAIN TUMORS ON MRI IMAGES USING DEEP LEARNING ARCHITECTURES

Abstract

A brain tumor is a dangerous neural illness produced by the strict growth of prison cells in the brain or head. The segmentation, analysis, and separation of unclean tumor parts from Magnetic Resonance Imaging (MRI) images are the main sources of anxiety. To report the segmented MRI images including tumor, the usage of computer-assisted methods is necessary. In this paper, a Convolutional Neural Network (CNN) approach is applied to identify brain cancers in MRI images. Two datasets are used in this study, namely Kaggle Brain MRI database and Figshare Brain MRI database. Models of deep CNN, consisting of VGG16, AlexNet, and ResNet, are utilized to extract deep features. The classification accuracies of the aforementioned Deep Learning (DL) networks are used to measure the efficiencies of the implemented systems. For the Kaggle database, AlexNet achieves 98% accuracy, VGG16 has 97% accuracy and ResNet has 66% accuracy. Among these networks, AlexNet has provided the highest level of accuracy. In the Figshare dataset, AlexNet and VGG16 both achieve 99% accuracy, and ResNet has 96% accuracy. In terms of accuracy, AlexNet and VGG16 outperform ResNet. These performances aid in the early detection of cancers before they cause physical harm such as paralysis and other complications.

Keywords

Supporting Institution

Not available.

References

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Details

Primary Language

English

Subjects

Computer Vision , Pattern Recognition , Deep Learning

Journal Section

Research Article

Authors

Samaneh Sarfarazı
0009-0007-5139-5662
Kuzey Kıbrıs Türk Cumhuriyeti

Önsen Toygar ^*
0000-0001-7402-9058
Kuzey Kıbrıs Türk Cumhuriyeti

Publication Date

December 12, 2023

Submission Date

August 9, 2023

Acceptance Date

September 25, 2023

Published in Issue

Year 2023 Volume: 26 Number: Özel Sayı

DOI

https://doi.org/10.17780/ksujes.1339884

IZ

https://izlik.org/JA84NR74HH

Cite

RIS / Bibtex

APA

Sarfarazı, S., & Toygar, Ö. (2023). CLASSIFICATION OF BRAIN TUMORS ON MRI IMAGES USING DEEP LEARNING ARCHITECTURES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(Özel Sayı), 1177-1186. https://doi.org/10.17780/ksujes.1339884