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

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2017-12-25
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Applied Sciences (Switzerland), Volume 8, issue 1
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%.
Description
Keywords
Brain tumor classification, Convolutional neural network, Glioblastoma, Magnetic resonance imaging
Other note
Citation
Khawaldeh, S, Pervaiz, U, Rafiq, A & Alkhawaldeh, R S 2017, ' Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks ', Applied Sciences (Switzerland), vol. 8, no. 1, 27 . https://doi.org/10.3390/app8010027