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Appl. Sci. 2018, 8(1), 27; doi:10.3390/app8010027

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

1
Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Girona, 17004 Girona, Spain
2
Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Burgundy, 21000 Dijon, France
3
Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
4
Department of Surgery, Virginia Commonwealth University, Richmond, VA 23298, USA
5
Department of Computer Information Systems, The University of Jordan, Aqaba 77110, Jordan
These authors contributed equally to this work and they both share the first authorship.
*
Author to whom correspondence should be addressed.
Received: 4 October 2017 / Revised: 15 November 2017 / Accepted: 18 December 2017 / Published: 25 December 2017
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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%. View Full-Text
Keywords: brain tumor classification; glioblastoma; convolutional neural network; magnetic resonance imaging brain tumor classification; glioblastoma; convolutional neural network; magnetic resonance imaging
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Khawaldeh, S.; Pervaiz, U.; Rafiq, A.; Alkhawaldeh, R.S. Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks. Appl. Sci. 2018, 8, 27.

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