التفاصيل البيبلوغرافية
| العنوان: |
Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks |
| المؤلفون: |
Khawaldeh, Saed, Pervaiz, Usama, Rafiq, Azhar, Alkhawaldeh, Rami S. |
| المصدر: |
Applied Sciences, 2018, vol. 8, núm. 1, p. 27 ; Articles publicats (D-ATC) |
| بيانات النشر: |
MDPI (Multidisciplinary Digital Publishing Institute) |
| سنة النشر: |
2017 |
| المجموعة: |
Universitat de Girona: DUGiDocs (UdG Digital Repository) |
| مصطلحات موضوعية: |
Imatge -- Segmentació, Imaging segmentation, Cervell -- Imatgeria per ressonància magnètica, Brain -- Magnetic resonance imaging, Imatgeria mèdica, Imaging systems in medicine, Cervell -- Tumors, Brain -- Tumors, Glioblastoma multiforme |
| الوصف: |
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% |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/eissn/2076-3417; http://hdl.handle.net/10256/18026 |
| الاتاحة: |
http://hdl.handle.net/10256/18026 |
| Rights: |
Attribution 4.0 International ; http://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.4A0F244E |
| قاعدة البيانات: |
BASE |