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

Applied Sciences

Rami S. Alkhawaldeh¹  Azhar Rafiq²  Saed Khawaldeh³  Usama Pervaiz³ 

[1] Department of Computer Information Systems, The University of Jordan, Aqaba 77110, Jordan;Department of Surgery, Virginia Commonwealth University, Richmond, VA 23298, USA;Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Girona, 17004 Girona, Spain;

关键词: brain tumor classification;    glioblastoma;    convolutional neural network;    magnetic resonance imaging;   

DOI  :  10.3390/app8010027

来源: DOAJ

【 摘 要 】

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%.

【 授权许可】

Unknown