Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks

Saed Khawaldeh*, Usama Pervaiz, Azhar Rafiq, Rami S. Alkhawaldeh

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

52 Citations (Scopus)
133 Downloads (Pure)

Abstract

In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.

Original languageEnglish
Article number27
JournalApplied Sciences (Switzerland)
Volume8
Issue number1
DOIs
Publication statusPublished - 25 Dec 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Brain tumor classification
  • Convolutional neural network
  • Glioblastoma
  • Magnetic resonance imaging

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